Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czechia. Institute of Scientific Instruments of the Czech Academy of Sciences, v.v.i., Královopolská 147, 612 64 Brno, Czechia. Author to whom any correspondence should be addressed.
Published: September 2018
AI Article Synopsis
Article Abstract
Objective: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and noisy/'P') of short single-lead ECGs recorded by wearable devices.
Approach: Various rhythm and morphology features are derived from the separate beats ('local' features) as well as the entire ECGs ('global' features) to represent short-term events and general trends respectively. Various types of atrial and ventricular activity, heart beats and, finally, ECG records are then recognised by a multi-level approach combining a support vector machine (SVM), decision tree and threshold-based rules.
Main Results: The proposed features are suitable for the recognition of 'A'. The method is robust due to the noise estimation involved. A combination of radial and linear SVMs ensures both high predictive performance and effective generalisation. Cost-sensitive learning, genetic algorithm feature selection and thresholding improve overall performance. The generalisation ability and reliability of this approach are high, as verified by cross-validation on a training set and by blind testing, with only a slight decrease of overall F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition seems to be the most difficult (test F1-measures: 0.90/'N', 0.81/'A' and 0.72/'O') due to high inter-patient variability and similarity with 'N'.
Significance: These study results contribute to multidisciplinary areas, focusing on creation of robust and reliable cardiac monitoring systems in order to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, consequently, ensure timely and effective treatment.
Background And Objective: Obstructive Sleep Apnea (OSA) is among the most sleep-related breathing disorders, capable of causing severe neurological and cardiovascular complications if left untreated. The conventional diagnosis of OSA relies on polysomnography, which involves multiple electrodes and expert supervision. A promising alternative is single-channel Electrocardiogram (ECG) based diagnosis due to its simplicity and relevance.
Background: Traditional screening methods, such as 12-lead electrocardiograms (ECGs) and Holter monitors, often fall short in detecting transient arrhythmias. However, advancements in wearable technology, like the Apple Watch®, enable real-time rhythm monitoring and specific arrhythmia detection through dedicated algorithms.
Case Description: A 60-year-old man with a history of arrhythmogenic cardiomyopathy and an implanted cardioverter-defibrillator (ICD), during a walk, experienced palpitations and dizziness; the Apple Watch® alerted him of an elevated heart rate.
Objective: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data.
Background: Heart rate variability (HRV) is a marker of autonomic function. However, the reliability of short-term HRV measurement in individuals with combat-related traumatic injury (CRTI) remains undetermined.
Methods: An intra- and inter-rater reliability study was conducted using a subsample (n = 35) of British servicemen with CRTI enrolled in the ongoing ADVANCE study.
Reverse total shoulder arthroplasty (rTSA) is an effective procedure to improve shoulder pain, range of motion, and function for a variety of conditions, including glenohumeral osteoarthritis and rotator cuff arthropathy. However, up to 22% of patients have persistent shoulder pain 12 to 24 months following rTSA, even in the absence of surgical complications. Currently, there are no widely accepted non-pharmacological treatments for persistent postoperative pain after rTSA.
