Decision tree for smart feature extraction from sleep HR in bipolar patients

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Bipolar Disorder Predictive Model: A Study to Analyze and Predict Emotional Change Using Physiological Signals

University of Minnesota M.S. thesis. July 2019. Major: Computer Science. Advisor: Arshia Khan. 1 computer file (PDF); viii, 95 pages.Bipolar disorder, a chronic mental illness, most common among people of ages 18 years and older, affects over 2.6 percent of the United States population alone. Although this illness cannot be cured, it can be managed by continuous tracking and monitoring. Hence, if a manic or depressive episode can be identified or predicted in its early stages, severe damage can be minimized, if not prevented. This thesis proposes the design of an objective sensor based system that is based on physiological predictors for the continuous and autonomous monitoring of bipolar patients. This system consists of a pulse rate sensor to record heart rate, and an electrodermal activity sensor to trace the emotional and cognitive state changes and does not rely completely on self-assessment or reporting. Furthermore, it investigates how psychological changes affect physiological responses, such as Heart rate variability (HRV) and Electrodermal activity (EDA). We conducted a study with 50 healthy participants, where each participant was subjected to a certain degree of image and audio induced emotion. Baseline and the emotional stimuli data was collected. Time- domain and non-linear analysis of HRV was then performed on the collected HRV data. In addition, EDA data analysis was performed by decomposing it into tonic and phasic components. We investigated the ability of HRV and EDA to identify the activity of the au- tonomic nervous system in response to emotional stimuli. Moreover, the extracted features from the data were then used to build machine learning models to predict the given psychological change in response to the emotional stimuli. Our results showed that these combination of HRV and EDA features from the study we conducted yielded an average accuracy of up to 73% with Support vector machine, and 68.3% with Discriminant analysis for predicting emotional change in healthy individuals

Tunteiden Havaitseminen Arkielämässä Koneoppimisen ja Puettavien Laitteiden Avulla

Tavoitteet. Tämän tutkimuksen tavoitteena on arvioida tunteiden havaitsemisen mahdollisuutta arkielämässä puettavien laitteiden ja koneoppimismallien avulla. Tunnetiloilla on tärkeä rooli päätöksenteossa, havaitsemisessa ja käyttäytymisessä, mikä tekee objektiivisesta tunnetilojen havaitsemisesta arvokkaan tavoitteen, sekä mahdollisten sovellusten että tunnetiloja koskevan ymmärryksen syventämisen kannalta. Tunnetiloihin usein liittyy mitattavissa olevia fysiologisia ja käyttäymisen muutoksia, mikä mahdollistaa koneoppimismallien kouluttamisen muutoksia aiheuttaneen tunnetilan havaitsemiseksi. Suurin osa tunteiden havaitsemiseen liittyvästä tutkimuksesta on toteutettu laboratorio-olosuhteissa käyttämällä tunteita herättäviä ärsykkeitä tai tehtäviä, mikä herättää kysymyksen siitä että yleistyvätkö näissä olosuhteissa saadut tulokset arkielämään. Vaikka puettavien laitteiden ja kännykkäkyselyiden kehittyminen on helpottanut aiheen tutkimista arkielämässä, tutkimusta tässä ympäristössä on vielä niukasti. Tässä tutkimuksessa itseraportoituja tunnetiloja ennustetaan koneoppimismallien avulla arkielämässä havaittavissa olevien tunnetilojen selvittämiseksi. Lisäksi tutkimuksessa käytetään mallintulkintamenetelmiä mallien hyödyntämien yhteyksien tunnistamiseksi. Metodit. Aineisto tätä tutkielmaa varten on peräisin tutkimuksesta joka suoritettiin osana Helsingin Yliopiston ja VTT:n Sisu at Work projektia, missä 82:ta tietotyöläistä neljästä suomalaisesta organisaatiosta tutkittiin kolmen viikon ajan. Osallistujilla oli jakson aikana käytettävissään mittalaitteet jotka mittasivat fotoplethysmografiaa (PPG), ihon sähkönjohtavuutta (EDA) ja kiihtyvyysanturi (ACC) signaaleita, lisäksi heille esitettiin kysymyksiä koetuista tunnetiloista kolmesti päivässä puhelinsovelluksen avulla. Signaalinkäsittelymenetelmiä sovellettiin signaaleissa esiintyvien liikeartefaktien ja muiden ongelmien korjaamiseksi. Sykettä (HR) ja sykevälinvaihtelua (HRV) kuvaavia piirteitä irroitettiin PPG signaalista, fysiologista aktivaatiota kuvaavia piirteitä EDA signaalista, sekä liikettä kuvaavia piirteitä ACC signaalista. Seuraavaksi koneoppimismalleja koulutettiin ennustamaan raportoituja tunnetiloja irroitetujen piirteiden avulla. Mallien suoriutumista vertailtiin suhteessa odotusarvoihin havaittavissa olevien tunnetilojen määrittämiseksi. Lisäksi permutaatiotärkeyttä sekä Shapley additive explanations (SHAP) arvoja hyödynnettiin malleille tärkeiden yhteyksien selvittämiseksi. Tulokset ja johtopäätökset. Mallit tunnetiloille virkeä, keskittynyt ja innostunut paransivat suoriutumistaan yli odotusarvon, joista mallit tunnetilalle virkeä paransivat suoriutumista tilastollisesti merkitsevästi. Permutaatiotärkeys korosti liike- ja HRV-piirteiden merkitystä, kun SHAP arvojen tarkastelu nosti esiin matalan liikkeen, matalan EDA:n, sekä korkean HRV:n merkityksen mallien ennusteille. Nämä tulokset ovat lupaavia korkean aktivaation positiivisten tunnetilojen havaitsemiselle arkielämässä, sekä nostavat esiin mahdollisia yhteyksiä jatkotutkimusta varten.Objectives. This study aims to evaluate feasibility of affect detection in daily life using wearable devices and machine learning models. Affective states play an important role in decision making, perception and behaviour, making objective detection of affective states a desirable goal both for potential applications and as a way to gain insight into affective phenomena. Affective states have been found to have measurable physiological and behavioral changes, which allows training of machine learning models for detecting the underlying affects. Majority of affect detection studies have been conducted in laboratory conditions using affect elicitation stimuli or tasks, raising the question whether results from these studies will generalize to daily life. Although development of wearable devices and mobile surveys have facilitated evaluation in the context of daily life, research here remains sparse. In this study, self-reported affective states are predicted using machine learning models to identify which affective states can be detected in daily life. Additionally, model interpretation methods will be used to identify which relationships the models found important for their predictions. Methods. Data for this thesis came from a study conducted as a part of Sisu at Work project between University of Helsinki and VTT, where 82 knowledge workers from four Finnish organizations were studied for a period of three weeks. During this period, the participants were queried by mobile surveys about their affective states thrice a day, while they also used wearable devices to record photoplethysmography (PPG), electrodermal activity (EDA) and accelerometry (ACC) signals. A signal processing pipeline was implemented to deal with movement artefacts and other issues with the data. Features describing heart rate (HR) and heart rate variation (HRV) were extraced from PPG, physiological activation from EDA and movement from ACC signals. Models were then fitted to predict the reported affective states using the extracted features. Model performance was compared against a baseline to identify which affects could be reliably detected, while permutation importance and Shapley additive explanations (SHAP) values were used to identify important relationships established by the models. Results and conclusions. Models for affective state vigor showed improvements over baseline with statistical significance, while improvements were also noted for affects focused and enthusiastic. Permutation importance highlighted the significance of movement and HRV features, while examination of SHAP values indicated that low movement, low EDA and high HRV impacted model predictions the most. These results indicate potential for detecting high activation affective states in daily life and propose potential relationships for future research

Applications Of Wearable Sensors In Delivering Biologically Relevant Signals

With continued advancements in wearable technologies, the applications for their use are growing. Wearable sensors can be found in smart watches, fitness trackers, and even our cellphones. The common applications in everyday life are usually step counting, activity tracking, and heart rate monitoring. However, researchers have developed ways to use these similar sensors for clinically relevant diagnostic measures, as well as, improved athletic training and performance. Two areas of interest for the use of wearable sensors are mental health diagnostics in children and heart rate monitoring during intense physical activity from new locations, which are discussed further in this thesis. About 20% of children will experience an anxiety or depressive disorder. These disorders, if left untreated, can lead to comorbidity, substance abuse, and even suicide. Current methods for diagnosis are time consuming and only offered to those most at risk (i.e., reported or referred by a teacher, doctor, or parent). For the children that do get referred to a specialist, the process is often inaccurate. Researchers began using mood induction task to observe behavioral responses to specific stimuli in hopes to improve the accuracy of diagnostics. However, these methods involve long hours of training and watching videos of the activities. Recently, a few studies have focused on using wearable sensors during mood induction tasks in hopes to pick up on relevant movements to distinguish those with and without an internalizing disorder. The first study presented in this thesis focuses on using wearable inertial measurement units during the ‘Bubbles’ mood induction task. A decision tree was developed to identify children with internalizing disorders, accuracy of this model was 71% based on leave-one-subject-out cross validation. The second study focuses on estimating heart rate using wearable photoplethysmography sensors at multiple body locations. Heart rate is an important vital sign used across a variety of contexts. For example, athletes use heart rate to determine whether they are hitting their desired heart rate zones during training and doctors can use heart rate as an early indicator of disease. With the advancements made in wearables, photoplethysmography can now be used to collect signals from devices anywhere on the body. However, estimating heart rate accurately during periods of intense physical activity remains a challenge due to signal corruption cause by motion artifacts. This study focuses on evaluating algorithms for accurately estimating heart rate from photoplethysmograms and determining the optimal body location for wear. A phase vocoder and Wiener filtering approach was used to estimate heart rate from the forearm, shank, and sacrum. The algorithm estimated heart rate to within 6.2 6.9, and 6.7 beats per minute average absolute error for the forearm, shank, and sacrum, respectively, across a wide variety of physical activities selected to induce varying levels of motion artifact

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio