QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESCWorking Group on Cardiac Cellular Electrophysiology

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity

Physiological measures

Journal ArticleHistorically, psychophysiological measures have made an invaluable contribution to personality psychology. Questions regarding interindividual differences and intraindividual changes in emotion, cognition, motivation, arousal, and attention are core topics within personality psychology, and these questions are particularly amenable to a psychophysiological approach

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

Objective: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. Methods: Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. Results: Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. Significance: Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization

The Effects of Heat and Massage Application on Autonomic Nervous System

∙ The authors have no financial conflicts of interest. © Copyright: Yonsei University College of Medicine 2011 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens

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

Assessing the relationship between resting autonomic nervous system functioning, social anxiety, and emotional autobiographical memory retrieval

Thesis advisor: Elizabeth KensingerIndividuals with social anxiety disorder (SAD) tend to have emotional memory biases in the encoding and retrieval of social memories. Research has shown reduced heart rate variability (HRV) in clinical populations suffering from anxiety, including social anxiety. Heightened sympathetic activation—as measured by the electrodermal activity (EDA)—has also been associated with anxiety disorders. The aim of the present study was to examine the relation between HRV, social anxiety, and re-experiencing of emotional autobiographical memories. 44 healthy young adults were recruited from the Boston College campus through SONA. Participants were given an online survey that instructed them to retrieve 40 specific events from the past in response to 40 socially relevant cues. For each event, participants were instructed to provide a brief narrative, make several ratings for the event (on a scale from 1-7), and indicate the specific emotions they experienced both at the time of retrieval and of the event. Approximately one month after the completion of the memory survey, participants engaged in a 2-hour memory retrieval session while undergoing psychophysiological monitoring (heart rate, skin conductance, and respiration). Following the retrieval task, participants completed self-report questionnaires of social anxiety symptom severity and trait emotion regulation strategy (i.e., tendency to reappraise or suppress emotions). The present study found that positive memories had higher re-experiencing ratings as compared to negative memories. Contrary to the original study hypothesis, however, there was no significant interaction between average re-experiencing (or arousal) ratings of positive or negative social autobiographical memories and SAD likelihood. A nonlinear, cubic relationship was found between one of three metrics of HRV and social anxiety symptom severity. A significant effect was found between skin conductance and SAD likelihood, which was likely driven by an almost significance difference in skin conductance between the SAD unlikely and the SAD very probable groups; these findings provide further insight into the relationship between autonomic nervous system (ANS) functioning and social anxiety. Further, the present results suggest the intriguing possibility that there may be a nonlinear relationship between HRV and severity of social anxiety. Future research with a larger sample size is needed to corroborate these findings.Thesis (BS) — Boston College, 2018.Submitted to: Boston College. College of Arts and Sciences.Discipline: Departmental Honors.Discipline: Psychology

Heart Rate Variability (HRV) analysis : a methodology for organizational neuroscience

Recently, the application of neuroscience methods and findings to the study of organizational phenomena has gained significant interest and converged in the emerging field of organizational neuroscience. Yet, this body of research has principally focused on the brain, often overlooking fuller analysis of the activities of the human nervous system and associated methods available to assess them. In this paper, we aim to narrow this gap by reviewing heart rate variability (HRV) analysis, which is that set of methods assessing beat-to-beat changes in the heart rhythm over time, used to draw inference on the outflow of the autonomic nervous system (ANS). In addition to anatomo- physiological and detailed methodological considerations, we discuss related theoretical, ethical, and practical implications. Overall, we argue that this methodology offers the opportunity not only to inform on a wealth of constructs relevant for management inquiries, but also to advance the organizational neuroscience research agenda and its ecological validity

Pupillary light reflex in children with autism spectrum disorders

Pupillary light reflex (PLR) refers to the phenomenon of pupil size changing with respect to retinal illumination. It's a noninvasive, functional test which can reveal a rich set of information about nervous system. Abnormal PLR in children with autism spectrum disorders (ASD) was previously reported in a small population. In this research, a series of systematic studies were carried out to investigate the association of atypical PLR with ASD in a large population. An experimental protocol was developed to measure PLR simultaneously with heart rate variability (HRV), a commonly used autonomic nervous system (ANS) measure. Our results indicate that variations of PLR and HRV are not associated in typically developing children. However, significant age effects on both PLR and HRV were observed in this population. In typically developing children, the resting pupil diameter increased with age significantly up to age 12. PLR constriction increased with age in children younger than 8 years old and reached a plateau thereafter. PLR latency decreased significantly from 6 to 9 years and stabilized thereafter. The average heart rate (AHR) decreased with age in typically developing children. Standard deviation of normal-to-normal intervals (SDNN) showed little change before 12 years of age but was increased in older children. High frequency normalized power (HFN) decreased with age in typically developing (TD) group. PLR and HRV were also measured in 152 children with ASD and 36 children with non-ASD neurodevelopmental disorders (NDDs). The results showed atypical PLR in the ASD group including longer PLR latency, reduced relative constriction amplitude, and shorter constriction/redilation time. Similar atypical PLR parameters were observed in the NDD group. The ASD and NDD groups had faster AHR than the TD group. The NDD group also showed a significantly faster AHR than the ASD group. The age effect on PLR latency which was observed in typically developing children of 6-9 years old was not observed in the ASD and NDD gro

Screening and monitoring of stress using biofeedback equipment

Biofeedback equipment is intended to train conscious regulation of normally sub-conscious processes like autonomic nervous system activities. The manufacturers claim that measurements made with the equipment are accurate enough for research purposes, but these claims have not been vigorously tested. The subconscious processes recorded with biofeedback equipment are often disturbed by stress, and the aim of this study was to determine if the markers of stress could be accurately determined with biofeedback equipment. The physiological processes that were screened were: Time and frequency domain heart rate variability (HRV) determined from blood-volume-pulse (BVP) Time and frequency domain HRV determined from electrocardiogram (ECG) The amplitude of the BVP Electromyographic (EMG) activity The pulse transit time Respiration rate and depth Skin conductivity Fingertip temperature Quantitative electroencephalographic (QEEG) activity The accuracy of the HRV measurements were tested by comparing them to readings made simultaneously with a gold-standard device (Actiheart), and the main findings were: The hardware capabilities of the two systems are comparable when it comes to registering heartbeats and calculating heart rate The frequency domain biofeedback HRV variables had relatively good correlations to the Actiheart results, but improvements are necessary Frequency domain HRV variables differ when calculated with fast Fourier transform or with autoregression The BVP signal is prone to movement artifact and other forms of interference The HRV measurements of both the biofeedback and Actiheart device were correlated to psychometric evaluations of anxiety and burnout, two conditions closely related to the concept of stress. The main findings were: Worry and anxiety can have a cardiac accelerating effect, largely mediated by vagal withdrawal A decrease in resting autonomic variability associated with anxiety Significant autonomic nervous system inflexibility occurs in the face of a cognitive stressor with increased anxiety An increase in vagal and a decrease in sympathetic cardiac control correlated with increased levels of vital exhaustion HRV assessment with specialized software such as Polar Precision Performance Software and the advanced HRV Analysis 1.1 software for windows (Biomedical Signal Analysis Group) were superior to assessments by means of the Biograph Infinity program Next it was investigated whether any association existed between levels of anxiety, burnout and that of Biograph-derived physiological indicators such as BVP amplitude, BVP HRV, ECG HRV, pulse transit time, EMG, fingertip temperature, respiration rate and amplitude, skin conductivity and QEEG levels. The overriding observations with increases in the levels of stress-related emotional conditions such as anxiety were that of a decrease in variability in almost all physiological functions assessed by Biograph. In conclusion, relatively good associations were found between certain, but not all, Biofeedback monitor results and that of other assessments of stress. The potential exists to develop a program which would accurately reflect stress levels.Dissertation (MSc)--University of Pretoria, 2012.School of Health Systems and Public Health (SHSPH)Unrestricte