LONG-TERM AMBULATORY ECG MONITORING AN ESSENTIAL DIAGNOSTIC TOOL FOR CURRENT TIMES

Ambulatory ECG (AECG) monitoring with diary correlation of symptoms has been proven to provide significant diagnostic, therapeutic and prognostic benefit with an arrhythmic cause of symptoms. Arrhythmias can range from premature atrial and ventricular complexes (APCs/ PVCs usually benign), to Atrial and Ventricular Fibrillation which causes significant morbidity and mortality. Symptoms such as palpitations, shortness of breath, chest pain and syncope are common during arrhythmias and their frequency determines the choice of investigation needed to diagnose the arrythmia. Arrhythmias can be a manifestation of many cardiac and non-cardiac diseases. These also include congenital diseases and are often missed due to inadequate monitoring. Since most arrhythmias are intermittent they are more likely to be detected during extended ECG monitoring. Other uses of ambulatory ECG devices include ST segment analysis, heart rate variability, signal averaged ECGs, diurnal QT and QTc analysis (including patients with long QT) (1) obstructive sleep apnea and vectorcardiography (2). These factors have been shown to have relation to significant cardiovascular diseases aiding the diagnosis of various arrhythmias. Syncope although mostly benign, could potentially be a consequence of a life-threatening arrhythmia in up to 20% patients(3). Nonetheless syncope poses a significant cause of disturbance in a patients’ life and definitive diagnosis is necessary to ensure patients well-being. ESC Guidelines on Syncope (2018) recommend further testing with AECG via Holter monitoring, wearable patch recorder, external and internal loop recorders etc. depending on the frequency after initial examination is negative for a definite cause. Atrial Fibrillation (AF) increases risk of stroke more than five times. Atrial Fibrillation diagnosed after stroke is an important hallmark of recurrent stroke risk. (7) Many studies have demonstrated post stroke AECG increases the chances of detecting AF (15% vs 5%) when compared to standard monitoring. An increase incidence in atrial arrythmias (atrial high rate episodes AHRE) has been seen in patients with Permanent Pacemakers which should be documented by AECG to be treated accordingly.(4). Uses can be prognostic if rate was to be monitored in AF to assess efficacy of rate control treatment and offer adequate anticoagulation according to the 2020 ESC atrial fibrillation guidelines. (8) Some limitations of twenty-four hours Holter monitoring have recently been overcome by improvements in hardware and software technology including adhesive patches and wireless telemetry. Newer adhesive patches are softer, waterproof and electrode free monitors which offer unprecedented mobility and ease of carrying out daily routine by the patient. They operate as either recorders or wireless streaming devices (5). These devices were safe and effective during the pandemic even when delivered home through mail to critically ill patients.(6) The advent of smart phones has added endless potential for recording through wireless Bluetooth transmission. Smart devices like the OMSHIRTtm have the added advantage of being comfortable to wear. Newer devices for example Cardiostat has been shown to offer equal quality tracings when compared to standard Holter monitoring, often up to the 99% sensitivity and specificity through better designed R wave (QRS) detection algorithms(7) (8)Studies have shown these newer devices to be easily operable and can even be mailed to patients homes for self-attachment with an equal efficacy to hospital applied machine (6). Many studies have shown a preference over intra cardiac monitors (ICM) due to these above mentioned advantages (9). The effectiveness of even longer recordings through Implantable Loop Recorder has also been satisfactory when following patients after Ablation therapy leading to practice updating guideline changes in rhythm management(10). A recent review article summarized that although physicians in the US had knowledge of how and when to offer monitoring devices based on the frequency of symptoms, they were often seen prescribing Holter monitoring due to familiarity. Data also showed that in case the initial investigation was inconclusive, the physician would still repeat the same investigation(3). In a country like Pakistan where there are limited resources, diagnosis and management of arrythmias still has a long way to go. This article sheds light on the need of utilizing the recommended available devices.

Comparative Assessment of Heart Rate Variability Obtained via Ambulatory ECG and Polar Heart Rate Monitors in Healthy Cats: A Pilot Study

Chronic exposure to stressful environments can negatively impact cats' health and welfare, affecting behavioral, autonomic, endocrine, and immune function, as with cats in shelters. Low-stress handling practices likely improve shelter cat welfare, but data supporting improved outcomes remain limited. Cardiac activity, particularly heart rate variability (HRV), is an indicator of stress and emotional state in humans and non-human animals, tracking important body functions associated with stress responsiveness, environmental adaptability, mental, and physical health. HRV studies in cats are limited, involving mainly anesthetized or restrained cats. This pilot study tested the feasibility of obtaining HRV data from unrestrained cats, using a commercially available cardiac monitoring system (Polar H10 with chest strap), compared with data from a traditional ambulatory electrocardiogram. Simultaneous data for the two systems were obtained for five adult cats. Overall, the Polar H10 monitor assessments of HRV were lower than the true HRV assessment by ambulatory ECG, except for SDNN. Correlation between the two systems was weak. Possible reasons for the lack of agreement between the two methods are discussed. At this time, our results do not support the use of Polar H10 heart rate monitors for studies of HRV in cats.

System for Continuous and Prolonged Ambulatory ECG Monitoring with Hosting and Visualization on the Cloud

In this paper, we propose investigating the ability to integrate a portable Electrocardiogram (ECG) device to commercial platforms to analyze and visualize information hosted in the cloud. Our ECG system based on the ADX8232 microchip was evaluated regarding its performance of recordings of a synthetic ECG signal for periods of 1, 2, 12, 24, and 36 h on six different cloud services to investigate whether it maintains reliable ECG records. Our results show that there are few cloud services capable of 24 h or longer ECG recordings. But some existing services are limited to small file sizes of less than 1,000,000 lines or 100 MB, or approximately 45 min of an ECG recording at a sampling rate of 360 Hz, making it difficult an extended time monitoring. Cloud platforms reveal some limitations of storage and visualization in order to provide support to health care specialists to access information related to a patient at any time.

Applications of Machine Learning in Ambulatory ECG

The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.

Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The best reconstruction algorithm is based on ANN, which reconstructs the actual ECG signal with high precision, as the results bring a high accuracy (RMS Error < 13 μV and CC > 99.7%) for the set of patients analyzed in this paper. This study supports the hypothesis that it is possible to reconstruct the Standard 12-Lead System using an aECG recording device with less leads.

Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

Each year 400,000 North Americans die from sudden cardiac death (SCD). T- wave alternans (TWA) refers to an alternating pattern in the T-wave portion of the surface electrocardiogram (ECG) and has been shown as a risk stratifier for SCD. These subtle changes in the T-waves are in the micro-volt scale and ambulatory ECG recordings usually contain biological noise. Also, data non-stationarity owing to heart rate variability and the amplitude variability in TWA magnitude can limit the accuracy of the detection techniques. This necessitates the need for robust detection algorithms for processing such non-stationary data. In this thesis, we have proposed an Empirical Mode Decomposition (EMD) based scheme combined with the Instantaneous Frequency (IF). EMD decomposes the signal into several monocomponent signals called Intrinsic Mode Functions (IMFs). IF extracted from these IMFs provides an accurate estimate of time varying frequency components and hence can aid during characterization of TWAs. In order to validate the performance of the proposed detection technique, the feature vectors extracted from the IMFs were fed to a linear discriminant analysis (LDA) classifier. The performance assessment was carried out using two datasets: (a) Synthetic TWAs: 72 signals obtained from publicly accessible Physionet database and (b) TWAs from patients: 55 ambulatory ECG signals obtained from the Toronto General Hospital. Using an unbiased leave-one-out cross validation strategy, maximum overall classification accuracies of 86.1% and 81.8% were achieved for TWA detection from synthetic and ambulatory ECG recordings respectively. In addition, the usability of the proposed technique has been investigated to assess its suitability for addressing another cardiovascular problem stroke. Atrial Fibrillation (AF) has been identified as a risk factor to increase the chances of stroke. The most common method in studying the complex AF electrograms is to employ dominant frequency (DF) analysis; however, due to signal non-stationarity DF does not always provide the best estimate of the atrial activation rate. As a result, analyzing the electrograms via EMD and IF has been investigated as the second contribution of this work.

Empirical Mode Decomposition and Analysis of Non-Stationary Cardiac Signals

Each year 400,000 North Americans die from sudden cardiac death (SCD). T- wave alternans (TWA) refers to an alternating pattern in the T-wave portion of the surface electrocardiogram (ECG) and has been shown as a risk stratifier for SCD. These subtle changes in the T-waves are in the micro-volt scale and ambulatory ECG recordings usually contain biological noise. Also, data non-stationarity owing to heart rate variability and the amplitude variability in TWA magnitude can limit the accuracy of the detection techniques. This necessitates the need for robust detection algorithms for processing such non-stationary data. In this thesis, we have proposed an Empirical Mode Decomposition (EMD) based scheme combined with the Instantaneous Frequency (IF). EMD decomposes the signal into several monocomponent signals called Intrinsic Mode Functions (IMFs). IF extracted from these IMFs provides an accurate estimate of time varying frequency components and hence can aid during characterization of TWAs. In order to validate the performance of the proposed detection technique, the feature vectors extracted from the IMFs were fed to a linear discriminant analysis (LDA) classifier. The performance assessment was carried out using two datasets: (a) Synthetic TWAs: 72 signals obtained from publicly accessible Physionet database and (b) TWAs from patients: 55 ambulatory ECG signals obtained from the Toronto General Hospital. Using an unbiased leave-one-out cross validation strategy, maximum overall classification accuracies of 86.1% and 81.8% were achieved for TWA detection from synthetic and ambulatory ECG recordings respectively. In addition, the usability of the proposed technique has been investigated to assess its suitability for addressing another cardiovascular problem stroke. Atrial Fibrillation (AF) has been identified as a risk factor to increase the chances of stroke. The most common method in studying the complex AF electrograms is to employ dominant frequency (DF) analysis; however, due to signal non-stationarity DF does not always provide the best estimate of the atrial activation rate. As a result, analyzing the electrograms via EMD and IF has been investigated as the second contribution of this work.