Developing An Atrial Activity-based Algorithm For Detection Of Atrial Fibrillation

Background - Atrial fibrillation (AF) is the most common cardiac arrhythmia. It affects an estimated 2.3 million United States citizens, and this number is only expected to increase as the general population ages. Automatic detection of AF could provide cardiologists with significant information for accurate and reliable diagnosis and monitoring of AF and is crucial for clinical therapy. However, monitoring AF remains an open area of research when the heart rate is controlled

Computer-Aided Clinical Decision Support Systems for Atrial Fibrillation

Clinical decision support systems (clinical DSSs) are widely used today for various clinical applications such as diagnosis, treatment, and recovery. Clinical DSS aims to enhance the end‐to‐end therapy management for the doctors, and also helps to provide improved experience for patients during each phase of the therapy. The goal of this chapter is to provide an insight into the clinical DSS associated with the highly prevalent heart rhythm disorder, atrial fibrillation (AF). The use of clinical DSS in AF management is ubiquitous, starting from detection of AF through sophisticated electrophysiology treatment procedures, all the way to monitoring the patient\u27s health during follow‐ups. Most of the software associated with AF DSS are developed based on signal processing, image processing, and artificial intelligence techniques. The chapter begins with a brief description of DSS in general and then introduces DSS that are used for various clinical applications. The chapter continues with a background on AF and some relevant mechanisms. Finally, a couple of clinical DSS used today in regard with AF are discussed, along with some proposed methods for potential implementation of clinical DSS for detection of AF, prediction of an AF treatment outcome, and localization of AF targets during a treatment procedure

Entropy and Frequency Analysis of New Electrocardiogram Lead Placement

Abstract - This is a preliminary study that explores ideal lead placements for quantification of atrial fibrillation. Data was collected at the Rochester Cardiopulmonary Group where two Atrial Fibrillation (AF) patients were monitored for one hour using a 12-lead Holter Recording setup. Lead placement was different than the clinical ECG lead placement. Two leads were placed at V1 and V2 followed by 5 leads to the left of the sternum and 5 to the right. For every lead pairing, the Shannon entropy as well as the Dominant Frequency of the bipolar signal were calculated and then compared based upon the lead locations (left only, right only, left and right). The results suggest that a reduced lead setup from a left-right combination could allow for an ambulatory AF detection device while preserving AF detection accuracy

Entropy & Frequency Analysis of New Electrocardiogram Lead Placement for Atrial Fibrillation Detection

Background - Atrial fibrillation (AF) is defined as a varying heart rate which can cause reduced blood flow to the body. It is the most common arrhythmia and it occurs when the electrical signals of the heart’s atria are disorganized. AF affects close to 3million people in the US and around 6million people in Europe. AF can cause many life-threatening problems, one of which is stroke

Exploring the feasibility of tensor decomposition for analysis of fNIRS signals: a comparative study with grand averaging method

The analysis of functional near-infrared spectroscopy (fNIRS) signals has not kept pace with the increased use of fNIRS in the behavioral and brain sciences. The popular grand averaging method collapses the oxygenated hemoglobin data within a predefined time of interest window and across multiple channels within a region of interest, potentially leading to a loss of important temporal and spatial information. On the other hand, the tensor decomposition method can reveal patterns in the data without making prior assumptions of the hemodynamic response and without losing temporal and spatial information. The aim of the current study was to examine whether the tensor decomposition method could identify significant effects and novel patterns compared to the commonly used grand averaging method for fNIRS signal analysis. We used two infant fNIRS datasets and applied tensor decomposition (i.e., canonical polyadic and Tucker decompositions) to analyze the significant differences in the hemodynamic response patterns across conditions. The codes are publicly available on GitHub. Bayesian analyses were performed to understand interaction effects. The results from the tensor decomposition method replicated the findings from the grand averaging method and uncovered additional patterns not detected by the grand averaging method. Our findings demonstrate that tensor decomposition is a feasible alternative method for analyzing fNIRS signals, offering a more comprehensive understanding of the data and its underlying patterns

Biological Signal Processing and Analysis for Healthcare Monitoring

Nowadays, portable and wireless wearable sensors have been commonly incorporated into the signal acquisition modules of healthcare monitoring systems [...

A Joint Time-Frequency and Matrix Decomposition Feature Extraction Methodology for Pathological Voice Classification

The number of people affected by speech problems is increasing as the modern world places increasing demands on the human voice via mobile telephones, voice recognition software, and interpersonal verbal communications. In this paper, we propose a novel methodology for automatic pattern classification of pathological voices. The main contribution of this paper is extraction of meaningful and unique features using Adaptive time-frequency distribution (TFD) and nonnegative matrix factorization (NMF). We construct Adaptive TFD as an effective signal analysis domain to dynamically track the nonstationarity in the speech and utilize NMF as a matrix decomposition (MD) technique to quantify the constructed TFD. The proposed method extracts meaningful and unique features from the joint TFD of the speech, and automatically identifies and measures the abnormality of the signal. Depending on the abnormality measure of each signal, we classify the signal into normal or pathological. The proposed method is applied on the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database which consists of 161 pathological and 51 normal speakers, and an overall classification accuracy of 98.6% was achieved