A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals

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Mälardalen University Press Dissertations No. 103

A PERSONALISED CASE-BASED STRESS DIAGNOSIS

SYSTEM USING PHYSIOLOGICAL SENSOR SIGNALS

Shahina Begum 2011

School of Innovation, Design and Engineering Mälardalen University Press Dissertations

No. 103

A PERSONALISED CASE-BASED STRESS DIAGNOSIS

SYSTEM USING PHYSIOLOGICAL SENSOR SIGNALS

Shahina Begum 2011

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ISSN 1651-4238

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Mälardalen University Press Dissertations No. 103

A PERSONALISED CASE-BASED STRESS DIAGNOSIS SYSTEM USING PHYSIOLOGICAL SENSOR SIGNALS

Shahina Begum

Akademisk avhandling

som för avläggande av teknologie doktorsexamen i datavetenskap vid Akademin för innovation, design och teknik kommer att offentligen försvaras

måndagen den 20 juni 2011, 13.35 i Pi, Mälardalens högskola, Västerås. Fakultetsopponent: Assistant Professor in Computer Science Stefania Montani,

University of Piemonte Orientale Amedeo Avogadro, Alessandria, Italy

Akademin för innovation, design och teknik Mälardalen University Press Dissertations

No. 103

A PERSONALISED CASE-BASED STRESS DIAGNOSIS SYSTEM USING PHYSIOLOGICAL SENSOR SIGNALS

Shahina Begum

Akademisk avhandling

som för avläggande av teknologie doktorsexamen i datavetenskap vid Akademin för innovation, design och teknik kommer att offentligen försvaras

måndagen den 20 juni 2011, 13.35 i Pi, Mälardalens högskola, Västerås. Fakultetsopponent: Assistant Professor in Computer Science Stefania Montani,

University of Piemonte Orientale Amedeo Avogadro, Alessandria, Italy

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Abstract

Stress is an increasing problem in our present world. It is recognised that increased exposure to stress may cause serious health problems if undiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, Electrocardiogram (ECG), finger temperature and respiration rate etc. during a number of exercises to diagnose stress-related disorders. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are few experts who are able to diagnose and predict stress-related problems. Therefore, a system that can help clinicians in diagnosing stress is important.

This research work has investigated Artificial Intelligence techniques for developing an intelligent, integrated sensor system to establish diagnosis and treatment plans in the psychophysiological domain. This research uses physiological parameters i.e., finger temperature (FT) and heart rate variability (HRV) for quantifying stress levels. Large individual variations in physiological parameters are one reason why case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Feature extraction methods to represent important features of original signals for case indexing are investigated. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness and uncertainty inherently existing in clinicians’ reasoning.

The evaluation of the approach is based on close collaboration with experts and measurements of FT and HRV from ECG data. The approach has been evaluated with clinicians and trial measurements on subjects (24+46 persons). An expert has ranked and estimated the similarity for all the subjects during classification. The result shows that the system reaches a level of performance close to an expert in both the cases. The proposed system could be used as an expert for a less experienced clinician or as a second opinion for an experienced clinician to supplement their decision making tasks in stress diagnosis.

ISBN 978-91-7485-018-5 ISSN 1651-4238

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Sammanfattning (Swedish Abstract)

Den ökande stressnivån hos många personer i vårt samhälle med allt högre krav och högt tempo har ett högt pris. Det är känt att en förhöjd stressnivå som inte blir upptäckt och korrekt identifierad/diagnostiserad och obehandlad under en längre tid kan leda till allvarliga hälsoeffekter för individen vilket kan leda till långvarig sjukskrivning. Stressrelaterade problem och sjukdomar är en stor samhällskostnad idag. Inom stressmedicinen mäter kliniker blodtryck, Elektrokardiogram (EKG), fingertemperatur och andning etc. under olika situationer för att diagnostisera stress. Stressdiagnos baserat fingertemperaturen (FT) är något som en skicklig klinker kan utföra vilket stämmer med forskningen inom klinisk psykofysiologi. I praktiken är det dock mycket svårt och mödosamt för en kliniker att i detalj följa och analysera långa serier av mätvärden och det finns endast få experter som är kompetent att diagnostisera och/eller förutsäga stressproblem. Därför är ett system, som kan hjälpa kliniker i diagnostisering av stress viktigt. Men de stora individvariationerna och bristen av precisa diagnosregler gör det svårt att använda datorbaserat system.

Detta forskningsarbete har tittat på flera tekniker och metoder inom artificiell intelligens för att hitta en väg fram till ett intelligent sensorbaserat system för diagnos och utformning av behandlingsplaner inom stressområdet. För att diagnostisera individuell stress har fallbaserat resonerande visat sig framgångsrikt, en teknik som gör det möjligt att återanvända erfarenhet och förklara beslut genom att hämta tidigare liknande fingertemperaturprofilerar. Vidare används ”fuzzy logic”, luddig logik så att hantera de inneboende vagheter i domänen. Metoder och algoritmer har utvecklats för detta.

Valideringen av ansatsen baseras på nära samarbete med experter och mätningar från tjugofyra respektive fyrtiosex användare. En erfaren kliniker har klassificerat alla fall och systemet har visat sig producera resultat nära en expert. Det föreslagna systemet kan användas som en referens för en mindre erfaren kliniker eller som en ”second opinion” för en erfaren kliniker i deras beslutsprocess. Dessutom har fingertemperatur visat sig passa bra för användning i hemmet, vid träning eller kontroll vilket blir möjligt ett datorbaserat stressklassificeringssystem på exempelvis en PC med en USB fingertemperaturmätare.

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Sammanfattning (Swedish Abstract)

Den ökande stressnivån hos många personer i vårt samhälle med allt högre krav och högt tempo har ett högt pris. Det är känt att en förhöjd stressnivå som inte blir upptäckt och korrekt identifierad/diagnostiserad och obehandlad under en längre tid kan leda till allvarliga hälsoeffekter för individen vilket kan leda till långvarig sjukskrivning. Stressrelaterade problem och sjukdomar är en stor samhällskostnad idag. Inom stressmedicinen mäter kliniker blodtryck, Elektrokardiogram (EKG), fingertemperatur och andning etc. under olika situationer för att diagnostisera stress. Stressdiagnos baserat fingertemperaturen (FT) är något som en skicklig klinker kan utföra vilket stämmer med forskningen inom klinisk psykofysiologi. I praktiken är det dock mycket svårt och mödosamt för en kliniker att i detalj följa och analysera långa serier av mätvärden och det finns endast få experter som är kompetent att diagnostisera och/eller förutsäga stressproblem. Därför är ett system, som kan hjälpa kliniker i diagnostisering av stress viktigt. Men de stora individvariationerna och bristen av precisa diagnosregler gör det svårt att använda datorbaserat system.

Detta forskningsarbete har tittat på flera tekniker och metoder inom artificiell intelligens för att hitta en väg fram till ett intelligent sensorbaserat system för diagnos och utformning av behandlingsplaner inom stressområdet. För att diagnostisera individuell stress har fallbaserat resonerande visat sig framgångsrikt, en teknik som gör det möjligt att återanvända erfarenhet och förklara beslut genom att hämta tidigare liknande fingertemperaturprofilerar. Vidare används ”fuzzy logic”, luddig logik så att hantera de inneboende vagheter i domänen. Metoder och algoritmer har utvecklats för detta.

Valideringen av ansatsen baseras på nära samarbete med experter och mätningar från tjugofyra respektive fyrtiosex användare. En erfaren kliniker har klassificerat alla fall och systemet har visat sig producera resultat nära en expert. Det föreslagna systemet kan användas som en referens för en mindre erfaren kliniker eller som en ”second opinion” för en erfaren kliniker i deras beslutsprocess. Dessutom har fingertemperatur visat sig passa bra för användning i hemmet, vid träning eller kontroll vilket blir möjligt ett datorbaserat stressklassificeringssystem på exempelvis en PC med en USB fingertemperaturmätare.

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Acknowledgements

I am deeply indebted to many people who, directly or indirectly, are responsible for this thesis coming into being. Especially, I am grateful to my main supervisor Peter Funk, for his constant inspiration, encouragement, and guidance, and lots of valuable ideas and discussions. It has always been fun and a great experience to work with you. I am also thankful to my assistant supervisor Ning Xiong, for all his help, encouragement, and intuitive ideas and for sharing interesting research thoughts during my work. I am grateful to my assistant supervisor, Bo von Schéele, who has always been helpful in providing me the domain knowledge to make this research possible. I wish to thank to my assistant supervisor Mia Folke, who has provided much encouragement and constructive suggestions to complete the work. The work presented in this thesis would not have been possible without the help of my supervisors. A special thanks to Maria Lindén, for her valuable support and cooperation during my PhD. I would like to thank my previous room colleague Ella Olsson and the fellow PhD students for all the great time together. Thanks to Laxmi Rao for her help in proofreading. I would like to thank to all who have participated as test subjects in the studies and thank to my bachelor and master’s thesis students who have contributed to the project. Thank to all the anonymous reviewers of my papers for their valuable feedback about the research. Many thanks are also forwarded to the staff at the department of IDT, Mälardalen University for always being helpful. In particular, thanks to Åsa Lundkvist, Annika Havbrandt, Mirko Senkovski, Magnus Otterskog, Else-Maj, Carola Ryttersson, Ingrid Runnérus, GunnarWidforss, Maria Ehn, Ellinor Karlsson and Malin Rosqvist and more, for your help. I would like to acknowledge the funding companies, especially the PBM Stressmedicine AB, Swedish Knowledge Foundation (KKS) and EU objective 2, NovaMedTech for funding the project. Further, I would like to express my gratitude to my teachers at Högskolan Dalarna, Sweden, Department of Computer Science and Engineering for their encouragement to start my research work.

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Acknowledgements

I am deeply indebted to many people who, directly or indirectly, are responsible for this thesis coming into being. Especially, I am grateful to my main supervisor Peter Funk, for his constant inspiration, encouragement, and guidance, and lots of valuable ideas and discussions. It has always been fun and a great experience to work with you. I am also thankful to my assistant supervisor Ning Xiong, for all his help, encouragement, and intuitive ideas and for sharing interesting research thoughts during my work. I am grateful to my assistant supervisor, Bo von Schéele, who has always been helpful in providing me the domain knowledge to make this research possible. I wish to thank to my assistant supervisor Mia Folke, who has provided much encouragement and constructive suggestions to complete the work. The work presented in this thesis would not have been possible without the help of my supervisors. A special thanks to Maria Lindén, for her valuable support and cooperation during my PhD. I would like to thank my previous room colleague Ella Olsson and the fellow PhD students for all the great time together. Thanks to Laxmi Rao for her help in proofreading. I would like to thank to all who have participated as test subjects in the studies and thank to my bachelor and master’s thesis students who have contributed to the project. Thank to all the anonymous reviewers of my papers for their valuable feedback about the research. Many thanks are also forwarded to the staff at the department of IDT, Mälardalen University for always being helpful. In particular, thanks to Åsa Lundkvist, Annika Havbrandt, Mirko Senkovski, Magnus Otterskog, Else-Maj, Carola Ryttersson, Ingrid Runnérus, GunnarWidforss, Maria Ehn, Ellinor Karlsson and Malin Rosqvist and more, for your help. I would like to acknowledge the funding companies, especially the PBM Stressmedicine AB, Swedish Knowledge Foundation (KKS) and EU objective 2, NovaMedTech for funding the project. Further, I would like to express my gratitude to my teachers at Högskolan Dalarna, Sweden, Department of Computer Science and Engineering for their encouragement to start my research work.

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I want to thank and acknowledge all my friends, relatives and brothers and sisters; especially thanks to my uncles Meftah Uddin Mahmud, Wahid Uddin Mahmud and Aunt Nargis Mahmud, brother Farhad Aziz and brother-in-law Mosabber Uddin Ahmed who have always inspired me throughout these years. I would like to remember my father Azizur Rahman, and father-in-law Gias Uddin Ahmed, for their everlasting support and encouragement for my work. Importantly, I would like to acknowledge the love and warmth of my mother Hasina Begum and mother-in-law Delwara Begum for all the success of my efforts. For last, and most important, I am grateful beyond words to my husband Mobyen Uddin Ahmed and my son Muhayen Uddin Ahmed, who have inspired me and supported me always in many ways. I doubt I would have been able to go through the process and finish the work without their invaluable love and support!

Shahina Begum, Västerås, June 2011

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Publications

Publications included in the thesis

I. Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments. Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Mia Folke, IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, IEEE, December, 2011

II. Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress. Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele, 8th_{European Conference on Case-based}

Reasoning workshop proceedings, p 113-122, Turkey 2006, Editor(s):M. Minor, September, 2006

III. Sensor Signal Processing to Extract Features from Finger Temperature in a Case-Based Stress Classification Scheme. Shahina Begum, 6th IEEE International Symposium on Intelligent Signal Processing (Special Session on Signal Processing in Bioengineering), Budapest, Hungary, August, 2009 IV. A Case-Based Decision Support System for Individual Stress Diagnosis

Using Fuzzy Similarity Matching. Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele, The Journal of Computational Intelligence (CI), vol. 25, nr 3, p180-195(16), Blackwell Publishing, August, 2009

V. A Decision Support System Based on ECG Sensor Signal in Determining Stress. Shahina Begum, Mobyen Uddin Ahmed and Peter Funk, Submitted to the journal of Expert Systems with Applications. Elsevier. ISSN: 0957-4174

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I want to thank and acknowledge all my friends, relatives and brothers and sisters; especially thanks to my uncles Meftah Uddin Mahmud, Wahid Uddin Mahmud and Aunt Nargis Mahmud, brother Farhad Aziz and brother-in-law Mosabber Uddin Ahmed who have always inspired me throughout these years. I would like to remember my father Azizur Rahman, and father-in-law Gias Uddin Ahmed, for their everlasting support and encouragement for my work. Importantly, I would like to acknowledge the love and warmth of my mother Hasina Begum and mother-in-law Delwara Begum for all the success of my efforts. For last, and most important, I am grateful beyond words to my husband Mobyen Uddin Ahmed and my son Muhayen Uddin Ahmed, who have inspired me and supported me always in many ways. I doubt I would have been able to go through the process and finish the work without their invaluable love and support!

Shahina Begum, Västerås, June 2011

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Publications

Publications included in the thesis

I. Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments. Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Mia Folke, IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, IEEE, December, 2011

II. Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress. Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele, 8th_{European Conference on Case-based}

Reasoning workshop proceedings, p 113-122, Turkey 2006, Editor(s):M. Minor, September, 2006

III. Sensor Signal Processing to Extract Features from Finger Temperature in a Case-Based Stress Classification Scheme. Shahina Begum, 6th IEEE International Symposium on Intelligent Signal Processing (Special Session on Signal Processing in Bioengineering), Budapest, Hungary, August, 2009 IV. A Case-Based Decision Support System for Individual Stress Diagnosis

Using Fuzzy Similarity Matching. Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele, The Journal of Computational Intelligence (CI), vol. 25, nr 3, p180-195(16), Blackwell Publishing, August, 2009

V. A Decision Support System Based on ECG Sensor Signal in Determining Stress. Shahina Begum, Mobyen Uddin Ahmed and Peter Funk, Submitted to the journal of Expert Systems with Applications. Elsevier. ISSN: 0957-4174

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Additional publications, not included in this thesis

Journals

- A Multi-Module Case Based Biofeedback System for Stress Treatment, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, Artificial Intelligence in Medicine, vol 51, nr 2, p107-115, ELSEVIER, February, 2011

- Case-Based Systems in the Health Sciences - A Case Study in the Field of Stress Management, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, WSEAS Transactions on Systems, vol 8, nr 1109-2777, p344-354, WSEAS, March, 2009

- Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, Transactions on Case-Based Reasoning on Multimedia Data, vol 1, nr 1, p3-19, IBaI Publishing, ISSN: 1864-9734, October, 2008 Articles in collection

- Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management, Shahina Begum, Mobyen Uddin Ahmed, Ning Xiong, Peter Funk, Computational Intelligence in Healthcare 4, p 159-189, Springer-Verlag Berlin Heidelberg , ISBN: 978-3-642-14463-9, Editor(s): Isabelle Bichindaritz, Sachin Vaidya, Ashlesha Jain and Lakhmi C. Jain, 2010 - Case-Based Reasoning for Medical and Industrial Decision Support Systems, Mobyen Uddin Ahmed, Shahina Begum, Ning Xiong, Peter Funk, Successful Case-based Reasoning Applications, p 7-52, Springer-Verlag, Germany, ISBN: 978-3-642-14077-8, Editor(s): Stefania Montani and Lakhmi Jain, 2010

Conferences and workshops

- ECG Sensor Signal Analysis to Represent Cases in a Case-based Stress Diagnosis System, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010), p 193-198, Corfu, Greece, November, 2010

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Additional publications, not included in this thesis

Journals

- A Multi-Module Case Based Biofeedback System for Stress Treatment, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, Artificial Intelligence in Medicine, vol 51, nr 2, p107-115, ELSEVIER, February, 2011

- Case-Based Systems in the Health Sciences - A Case Study in the Field of Stress Management, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, WSEAS Transactions on Systems, vol 8, nr 1109-2777, p344-354, WSEAS, March, 2009

- Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, Transactions on Case-Based Reasoning on Multimedia Data, vol 1, nr 1, p3-19, IBaI Publishing, ISSN: 1864-9734, October, 2008 Articles in collection

- Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management, Shahina Begum, Mobyen Uddin Ahmed, Ning Xiong, Peter Funk, Computational Intelligence in Healthcare 4, p 159-189, Springer-Verlag Berlin Heidelberg , ISBN: 978-3-642-14463-9, Editor(s): Isabelle Bichindaritz, Sachin Vaidya, Ashlesha Jain and Lakhmi C. Jain, 2010 - Case-Based Reasoning for Medical and Industrial Decision Support Systems, Mobyen Uddin Ahmed, Shahina Begum, Ning Xiong, Peter Funk, Successful Case-based Reasoning Applications, p 7-52, Springer-Verlag, Germany, ISBN: 978-3-642-14077-8, Editor(s): Stefania Montani and Lakhmi Jain, 2010

Conferences and workshops

- ECG Sensor Signal Analysis to Represent Cases in a Case-based Stress Diagnosis System, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010), p 193-198, Corfu, Greece, November, 2010

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Additional Publications

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- Intelligent Stress Management System, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, Maria Lindén, Mia Folke, Medicinteknikdagarna, p 83, Västerås, Sweden, September, 2009

- A Multi-Modal Case-Based System for Clinical Diagnosis and Treatment in Stress Management, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Seventh Workshop on Case-Based Reasoning in the Health Sciences, p 215-224, Seattle, Washington, USA, Editor(s):Sarah Jane Delany, July, 2009

- Diagnosis and Biofeedback System for Stress, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele, Maria Lindén, Mia Folke, 6th international workshop on Wearable Micro and Nanosystems for Personalised Health (pHealth), p 48, Oslo, Norway, June, 2009

- An Overview on Recent Case-Based Reasoning Systems in the Medicine, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, 25th annual workshop of the Swedish Artificial Intelligence Society, p 65, Linköping University Electronic Press, Linköpings universitet, Linköping, May, 2009 - Fuzzy Rule-Based Classification to Build Initial Case Library for Case-Based Stress Diagnosis, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, 9th IASTED international conference on Artificial Intelligence and Applications (AIA). IASTED, Innsbruck, Austria, Editor(s):M.H. Hamza, February, 2009

- Multi-Modal and Multi-Purpose Case-based Reasoning in the Health Sciences, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, 8th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED), February 21-23, 2009, p 378-383, WSEAS press, Cambridge, UK, Editor(s):Leon Trilling et al, February, 2009 - A Three Phase Computer Assisted Biofeedback Training System Using Case-Based Reasoning, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, 9th European Conference on Case-based Reasoning workshop proceedings, p 57-68, Trier, Germany, Editor(s):Martin Schaaf, August, 2008

- Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), 8th Industrial Conference,

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ICDM, p 128-144, IBaI, Leipzig, Germany, Editor(s):Isabelle Bichindaritz, Petra Perner & Linda Shapiro, July, 2008

- A Computer-Based System for the Assessment and Diagnosis of Individual Sensitivity to Stress in Psychophysiology, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Mia Folke, Bo von Schéele (PBMStressMedicine AB), Medicinteknikdagarna, p 55, Stockholm, November, 2007

- Similarity of Medical Cases in Health Care Using Cosine Similarity and Ontology, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), 5th Workshop on CBR in the Health Sciences, ICCBR, p 263-272, Springer LNCS, Belfast, Northern Ireland, Editor(s):David C. Wilson and Deepak Khemani, August, 2007

- Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), In proceedings of 7th International Conference on Case-Based Reasoning, p 478-491, Springer, Belfast, Northern Ireland, Editor(s):Rosina O.Weber and Michael M. Richter, August, 2007

- Individualized Stress Diagnosis Using Calibration and Case-Based Reasoning, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, p 59-69, Borås, Sweden, Editor(s):Löfström et al., May, 2007

- Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease, Shahina Begum, Jerker Westin (Högskolan Dalarna), Peter Funk, Mark Dougherty (Högskolan Dalarna), annual workshop of the Swedish Artificial Intelligence Society (SAIS), p 67-72, Umeå, Editor(s):P. Eklund, M. Minock, H. Lindgren, May, 2006

Technical reports

- Bibliometric Profiling of a Group: A Discussion on Different Indicators, Mobyen Uddin Ahmed, Shahina Begum, Technical Report, MRTC, February, 2011

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- Intelligent Stress Management System, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, Maria Lindén, Mia Folke, Medicinteknikdagarna, p 83, Västerås, Sweden, September, 2009

- A Multi-Modal Case-Based System for Clinical Diagnosis and Treatment in Stress Management, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Seventh Workshop on Case-Based Reasoning in the Health Sciences, p 215-224, Seattle, Washington, USA, Editor(s):Sarah Jane Delany, July, 2009

- Diagnosis and Biofeedback System for Stress, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele, Maria Lindén, Mia Folke, 6th international workshop on Wearable Micro and Nanosystems for Personalised Health (pHealth), p 48, Oslo, Norway, June, 2009

- An Overview on Recent Case-Based Reasoning Systems in the Medicine, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, 25th annual workshop of the Swedish Artificial Intelligence Society, p 65, Linköping University Electronic Press, Linköpings universitet, Linköping, May, 2009 - Fuzzy Rule-Based Classification to Build Initial Case Library for Case-Based Stress Diagnosis, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, 9th IASTED international conference on Artificial Intelligence and Applications (AIA). IASTED, Innsbruck, Austria, Editor(s):M.H. Hamza, February, 2009

- Multi-Modal and Multi-Purpose Case-based Reasoning in the Health Sciences, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, 8th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED), February 21-23, 2009, p 378-383, WSEAS press, Cambridge, UK, Editor(s):Leon Trilling et al, February, 2009 - A Three Phase Computer Assisted Biofeedback Training System Using Case-Based Reasoning, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele, 9th European Conference on Case-based Reasoning workshop proceedings, p 57-68, Trier, Germany, Editor(s):Martin Schaaf, August, 2008

- Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity, Mobyen Uddin Ahmed, Shahina Begum, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), 8th Industrial Conference,

xi

ICDM, p 128-144, IBaI, Leipzig, Germany, Editor(s):Isabelle Bichindaritz, Petra Perner & Linda Shapiro, July, 2008

- A Computer-Based System for the Assessment and Diagnosis of Individual Sensitivity to Stress in Psychophysiology, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Mia Folke, Bo von Schéele (PBMStressMedicine AB), Medicinteknikdagarna, p 55, Stockholm, November, 2007

- Similarity of Medical Cases in Health Care Using Cosine Similarity and Ontology, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), 5th Workshop on CBR in the Health Sciences, ICCBR, p 263-272, Springer LNCS, Belfast, Northern Ireland, Editor(s):David C. Wilson and Deepak Khemani, August, 2007

- Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), In proceedings of 7th International Conference on Case-Based Reasoning, p 478-491, Springer, Belfast, Northern Ireland, Editor(s):Rosina O.Weber and Michael M. Richter, August, 2007

- Individualized Stress Diagnosis Using Calibration and Case-Based Reasoning, Shahina Begum, Mobyen Uddin Ahmed, Peter Funk, Ning Xiong, Bo von Schéele (PBMStressMedicine AB), Proceedings of the 24th annual workshop of the Swedish Artificial Intelligence Society, p 59-69, Borås, Sweden, Editor(s):Löfström et al., May, 2007

- Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease, Shahina Begum, Jerker Westin (Högskolan Dalarna), Peter Funk, Mark Dougherty (Högskolan Dalarna), annual workshop of the Swedish Artificial Intelligence Society (SAIS), p 67-72, Umeå, Editor(s):P. Eklund, M. Minock, H. Lindgren, May, 2006

Technical reports

- Bibliometric Profiling of a Group: A Discussion on Different Indicators, Mobyen Uddin Ahmed, Shahina Begum, Technical Report, MRTC, February, 2011

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- Heart Rate and Inter-beat Interval Computation to Diagnose Stress, Mobyen Uddin Ahmed, Shahina Begum, Mohd. Siblee Islam (external), Technical Report, MRTC, September, 2010

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Development of a Stress Questionnaire: A Tool for Diagnosing Mental Stress, Shahina Begum, Mobyen Uddin Ahmed, Bo von Schéele (PBMStressMedicine AB), Erik Olsson (PBM Sweden AB), Peter Funk, Technical Report, MRTC, June, 2010

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Figures

Figure 2.1-1 Physiology of the stress response ... 19

Figure 2.1-2 General overview of the stress response ... 20

Figure 2.2-1 Biofeedback training using physiological sensor signals ... 21

Figure 2.2-2 Variations on finger temperature measurement with stress in the different test phases. Condition 3 and 5 followed by condition 1(baseline) and condition 2(deep breath) are stress and relax and finally, condition 5 and 6 show the math stress and relax ... 23

Figure 2.2-3 Individual A. Variations of the representative temperature dependent on the individual ... 24

Figure 2.2-4 Individual B. Variations of the representative temperature dependent on the individual ... 24

Figure 2.2-5 Finger temperature for a person before (orange) and after (blue) lunch ... 25

Figure 2.2-6 The person cannot eliminate stressful thoughts in the ‘relax condition’ in step 4 ... 25

Figure 2.2-7 A student before the thesis presentation ... 26

Figure2.2-8 ECG signal in QRS complex wave form ... 28

Figure 2.2-9 Variations of heart rate signal during different test conditions ... 29

Figure 3.1-1 Case structure ... 35

Figure 3.1-2 Problem part of a case ... 35

Figure 3.1-3 Solution part of a case ... 35

Figure 3.1-4 Problem and solution part of a case resides in the problem and solution space respectively ... 36

Figure 3.1-5 Example of a case library in a CBR system ... 37

Figure 3.1-6 Aamodt and Plaza’s CBR cycle (Aamodt, 1994) ... 38

Figure 3.2-1 Example presented in crisp set ... 41

Figure 3.2-2 Example presented in fuzzy set ... 41

Figure 3.2-3 Example of fuzzy union ... 43

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- Heart Rate and Inter-beat Interval Computation to Diagnose Stress, Mobyen Uddin Ahmed, Shahina Begum, Mohd. Siblee Islam (external), Technical Report, MRTC, September, 2010

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Development of a Stress Questionnaire: A Tool for Diagnosing Mental Stress, Shahina Begum, Mobyen Uddin Ahmed, Bo von Schéele (PBMStressMedicine AB), Erik Olsson (PBM Sweden AB), Peter Funk, Technical Report, MRTC, June, 2010

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Figures

Figure 2.1-1 Physiology of the stress response ... 19

Figure 2.1-2 General overview of the stress response ... 20

Figure 2.2-1 Biofeedback training using physiological sensor signals ... 21

Figure 2.2-2 Variations on finger temperature measurement with stress in the different test phases. Condition 3 and 5 followed by condition 1(baseline) and condition 2(deep breath) are stress and relax and finally, condition 5 and 6 show the math stress and relax ... 23

Figure 2.2-3 Individual A. Variations of the representative temperature dependent on the individual ... 24

Figure 2.2-4 Individual B. Variations of the representative temperature dependent on the individual ... 24

Figure 2.2-5 Finger temperature for a person before (orange) and after (blue) lunch ... 25

Figure 2.2-6 The person cannot eliminate stressful thoughts in the ‘relax condition’ in step 4 ... 25

Figure 2.2-7 A student before the thesis presentation ... 26

Figure2.2-8 ECG signal in QRS complex wave form ... 28

Figure 2.2-9 Variations of heart rate signal during different test conditions ... 29

Figure 3.1-1 Case structure ... 35

Figure 3.1-2 Problem part of a case ... 35

Figure 3.1-3 Solution part of a case ... 35

Figure 3.1-4 Problem and solution part of a case resides in the problem and solution space respectively ... 36

Figure 3.1-5 Example of a case library in a CBR system ... 37

Figure 3.1-6 Aamodt and Plaza’s CBR cycle (Aamodt, 1994) ... 38

Figure 3.2-1 Example presented in crisp set ... 41

Figure 3.2-2 Example presented in fuzzy set ... 41

Figure 3.2-3 Example of fuzzy union ... 43

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Figures

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Figure 3.2-5 Example of fuzzy complement ... 44

Figure 3.3-1 The FFT decomposition ... 45

Figure 3.4-1 The R2_{explains how good the model is, where Y is the depend variable and X} is the independent variable ... 46

Figure 3.4-2 Square of the correlation coefficient or Goodness-of-fit ... 47

Figure 3.4-3 Possible outcomes of a classifier ... 49

Figure 4-0-1 A general diagram for developing computer-based medical decision support systems ... 53

Figure 4.1-1 Procedure used to create an individual stress profile ... 54

Figure 4.1-2 An example of the finger temperature measurement during the six different steps in the calibration phase ... 55

Figure 4.1-3 Variations of HR signal during different test conditions ... 56

Figure 4.2-1 Example of visualisations of temperature change, X-axis minutes, Y-axis in degree Celsius ... 57

Figure 4.2-2 Changes in FT data against time during different stress and non-stress conditions ... 58

Figure 4.2-3 Example of an RR interval or inter-beat interval signal ... 61

Figure 4.2-4 The normalized unit of power spectral density ... 63

Figure 4.3-1 Schematic diagram of the steps in stress diagnosis system ... 69

Figure 4.3-2 Overview of the case-based system based on ECG sensor signal ... 71

Figure 4.3-3 Similarity matching of a current case with previous cases is presented in a ranked list of cases as solution. ... 72

Figure 5.1-1 Comparison results of the classification for the three parameters (i.e. FT, HR and RR average of all the groups) ... 78

Figure 5.1-2 Comparison results of the classification for the five test groups (i.e. TG-A, TG-B, TG-C, TG-D, TG-E and average of all the groups) ... 81

Figure 5.1-3 Sensitivity and specificity test using Time, Frequency and Time-frequency (i.e., multi-domain) domain features ... 83

Figure 5.1-4 Sensitivity and specificity test based on the expert’s and a senior clinician’s classification ... 84

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Acronyms

AI Artificial Intelligence ANN Artificial Neural Networks ACTH Adrenocorticotropin Hormone ANS Autonomous Nervous System CBR Case-Based Reasoning

CRH Corticotropin-Releasing Hormone DSS Decision Support System

DFT Discrete Fourier Transformation ECG Electrocardiogram

EMG Electromyography EMG Electromyography EEG Electroencephalography ETCO2 End-tidal Carbon Dioxide FFT Fast Fourier Transformation FT Finger Temperature HR Heart Rate

HRV Heart Rate Variability HF High Frequency IBI Inter-beat Interval kNN K-Nearest Neighbour LF Low Frequency MF Membership Function

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