Ing. Jakub Schneider, Ph.D.

In this work, we investigated the accuracy of chronotype (CHT) estimation from actigraphy (ACT) while evaluating the required recording length and stability over time. CHTs have an important role in chronobiological and sleep research. In outpatient studies, CHTs are typically evaluated by questionnaires. Alternatively, ACT provides potential means for measuring CHT characteristics objectively, which opens many applications in chronobiology research. However, studies providing objective, critical evaluation of agreement between questionnaire-based and ACT-based CHTs are lacking. We recorded 3-months of ACT and collected MEQ, and MCTQ results from 122 women. Regression models were applied to evaluate the questionnaire-based CHTs scores using selected ACT features. Changes in predictive strength were evaluated based on ACT recordings of different duration. The ACT was significantly associated with the questionnaire-based CHT, and the best single-feature-based models explained 37% of the variability (R2) for MEQ (p<.001), 47% for mid-sleep time MCTQ-MSFsc (p<.001), and 19% for social jetlag MCTQ-SJLrel (p<.001). Concerning stability in time, the Mid-sleep and Acrophase features showed high levels of stability (test-retest R ~ 0.8), and ACT-based MSFscacti and SJLrelacti showed high temporal variability (test-retest R ~ 0.45). Concerning required recording length, features estimated from recordings with 3-week and longer observation periods had sufficient predictive power on unseen data. Additionally, our data showed that the subjectively reported extremes of the MEQ, MCTQ-MSFsc, and MCTQ-SJLrel are commonly overestimated compared to objective activity peak and middle of sleep differences measured by ACT. Such difference may be associated with CHT time-variation. As ACT is considered accurate in sleep-wake cycle detection, we conclude that ACT-based chronotyping is appropriate for large-scale studies, especially where higher temporal variability in CHT is expected.

Background This paper describes the development of a mobile app for diabetes mellitus (DM) control and self-management and presents the results of long-term usage of this system in the Czech Republic. DM is a chronic disease affecting large numbers of people worldwide, and this number is continuously increasing. There is massive potential to increase adherence to self-management of DM with the use of smartphones and digital therapeutics interventions. Objective This study aims to describe the process of development of a mobile app, called Mobiab, for DM management and to investigate how individual features are used and how the whole system benefits its long-term users. Using at least 1 year of daily records from users, we analyzed the impact of the app on self-management of DM. Methods We have developed a mobile app that serves as an alternative form to the classic paper-based protocol or diary. The development was based on cooperation with both clinicians and people with DM. The app consists of independent individual modules. Therefore, the user has the possibility to use only selected features that they find useful. Mobiab was available free of charge on Google Play Store from mid-2014 until 2019. No targeted recruitment was performed to attract users. Results More than 500 users from the Czech Republic downloaded and signed up for the mobile app. Approximately 80% of the users used Mobiab for less than 1 week. The rest of the users used it for a longer time and 8 of the users produced data that were suitable for long-term analysis

OBJECTIVE: Bipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls into their respective groups. METHODS: Ninety-day actigraphy records from 25 inter-episode BD patients (i.e. Montgomery-Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) <15) and 25 sex- and age-matched healthy controls (HC), were used in order to identify latent actigraphic biomarkers capable of discriminating between BD patients and HC. Mean values and time-variations of a set of standard actigraphy features were analysed and further validated using the random forest classifier. RESULTS: Using all actigraphy features, this method correctly assigned 88% (sensitivity=85%, specificity=91%) of BD patients and HC to their respective group. The classification success may be confounded by differences in employment between BD patients and HC. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen’s d=1.33), 79% of the subjects (sensitivity=76%, specificity=81%) were correctly classified. CONCLUSION: A machine learning actigraphy-based model was capable of distinguishing between inter-episode BD patients and HC solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HC while being less affected by employment status.

Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully applied in a myriad of other physiological frameworks. This is a very difficult task, since actigraphy records are highly non-stationary and corrupted with artifacts (no activity). The method devised uses a preprocessing stage to extract epochs of activity, and then applies a quantification measure, Slope Entropy, recently proposed, which outperforms the most common entropy measures used in biomedical time series. The results confirm the feasibility of the approach proposed, since the three states that are involved in BD, depression, mania, and remission, can be significantly distinguished.

Introduction The study investigates the association between circadian phenotype (CP), its stability (interdaily stability - IS) and physical activity (PA) in a weight loss (WL) programme. Methods Seventy-five women in WL conservative treatment (BMI >= 25 kg/m2) were measured (for about 3 months in between 2016 and 2018) by actigraphy. Results We observed a difference in time of acrophase (p = 0.049), but no difference in IS (p = 0.533) between women who lost and did not lose weight. There was a difference in PA (mesor) between groups of women who lost weight compared to those who gained weight (p = 0.007). There was a relationship between IS and PA parametres mesor: p0.001; and the most active 10 h of a day (M10): p < 0.001 - the more stable were women in their rhythm, the more PA they have. Besides confirming a relationship between PA and WL, we also found a relation between WL and CP based on acrophase. Although no direct relationship was found for the indicators of rhythm stability (IS), they can be considered very important variables because of their close connection to PA - a main factor that contributes to the success of the WL programme. Discussion According to the results of the study, screening of the CP and its stability may be beneficial in the creation of an individualized WL plan.

BACKGROUND: Extracellular microelectrode recording (MER) is a prominent technique for studies of extracellular single-unit neuronal activity. In order to achieve robust results in more complex analysis pipelines, it is necessary to have high quality input data with a low amount of artifacts. We show that noise (mainly electromagnetic interference and motion artifacts) may affect more than 25% of the recording length in a clinical MER database. NEW METHOD: We present several methods for automatic detection of noise in MER signals, based on (i) unsupervised detection of stationary segments, (ii) large peaks in the power spectral density, and (iii) a classifier based on multiple time- and frequency-domain features. We evaluate the proposed methods on a manually annotated database of 5735 ten-second MER signals from 58 Parkinson's disease patients. COMPARISON WITH EXISTING METHODS: The existing methods for artifact detection in single-channel MER that have been rigorously tested, are based on unsupervised change-point detection. We show on an extensive real MER database that the presented techniques are better suited for the task of artifact identification and achieve much better results. RESULTS: The best-performing classifiers (bagging and decision tree) achieved artifact classification accuracy of up to 89% on an unseen test set and outperformed the unsupervised techniques by 5–10%. This was close to the level of agreement among raters using manual annotation (93.5%). CONCLUSION: We conclude that the proposed methods are suitable for automatic MER denoising and may help in the efficient elimination of undesirable signal artifacts.

This paper shows how the Mobiab system is useful for patients and which features are usually used. 18 users of the Mobiab system have been chosen. The data for this study was collected since February 2015 to February 2016. Both patients suffering from diabetes mellitus and non-diabetic users of the Mobiab system participated in the study. The results indicate that for more than a half of users is the Mobiab system convenient and used most of all functionalities of the system. One third of the users use only limited subset of functionalities - e.g. glucose and insulin monitoring.

The number of patients suffering from Parkinson’s disease is increasing rapidly due to population aging. While traditional medications-based palliative therapy is successful in early stages, deep brain stimulation (DBS) may be used as an alternative treatment in later stages. After DBS implantation, the therapy typically consists of electrical stimulation and reduced medication. In order to provide good clinical outcome, a balance has to be found between medication and stimulation parameters, this is usually done as follows: First, Unified Parkinson's Disease Rating Scale (UPDRS) scoring is performed, second patients are supposed to fill subjective diaries during a specific period. This study shows that these diaries are useful as therapy progression indicator. Feel scores based on diaries and sleep time were examined with respect to DBS stimulation and medication. The results confirmed the positive effect of both therapy components - stimulation as well as medication - on patient feel scores. Furthermore, a positive correlation was observed between stimulation energy and sleep duration.

This paper discusses the possibility of usage of dynamic biometric signatures (DBS) as an alternative of “advanced” electronic signatures. The authors refer to the issues of uniqueness of DBS in terms of individual’s characteristic motion, and hence the validity of parameters required for methods of identification of users and their authentication in the “electronic” world. Furthermore the authors focused on the issue of the use of DBS in connection with legal acts of people and the integration of authentication tools, based on DBS in the legal systems of the EU countries. The conclusions set out in the paper are supported by the results of experiments, when panelists created their biometric samples under different conditions that may have a significant impact, especially for set-up setting of FNMR parameter (False Non-Match Rate).

Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.

Biometric authentication methods are increasingly proving to be a sensible compromise between the demands on the user and/or the authentication tools without reducing the level of security. Dynamic biometric signature (DBS) systems record data from the handwritten signature using special tools which enable an analysis of both the static and dynamic properties associated with the typical behaviour of the signing individual.

Biometric authentication methods are increasingly proving to be a sensible compromise between the demands on the user and/or the authentication tools without reducing the level of security. Dynamic biometric signature (DBS) systems record data from the handwritten signature using special tools which enable an analysis of both the static and dynamic properties associated with the typical behaviour of the signing individual.

Surgical treatment of epilepsy is based on exact localization of “epileptogenic area”. During presurgical examination tracing of interictal discharges (spikes) propagation is used as additional information about location of this area. The tracking may be done by estimation of delay between spikes in EEG channels. Medical EEG recordings are not recorded with high sampling frequency. That may cause problem, because sometimes the speed of spike propagation is so high, that the delay lies under sampling resolution. The main goal of this preliminary study is to use coherence method on intracranial EEG data and examine if it is able to estimate delay between (IED) even under sampling resolution with reasonable error. For examination simulated EEG signals, with known delays are used. The simulation is done by using IED pattern made from “golden standard” epileptic discharges. Preliminary result made on simulated data shows that this coherence based method is able to examine delays even under sampling resolution for signals with amplitude signal-to-noise ratio (SNR) higher than 10 dB. Such high values of SNR are reached in real EEG recording only by the biggest spikes. Therefore this method is not better for purposes of spike propagation tracking than for example cross-correlation based methods.