Effectiveness of a handheld remote ECG monitor

This present study deals with designing a real-time remote handheld ECG monitoring system and evaluating its potential usefulness in early detection of heart conduction problems. The raw ECG recordings were sent by the handheld monitor (client) to a remote server, which performed an on-line ECG analysis and sent the results back to the client. Real-time feedback provided to the client included display of ECG, results of ECG analysis and alarms (if required). The objective of this work was to determine its effectiveness in real-time identification of particular pattern preceding ventricular fibrillation. The remote server identified the occurrence of QRS complex and premature ventricular contractions and monitored ECG for ventricular tachycardia and variations in heart rate variability indices. The sensitivity and specificity of the QRS detection to ECG recordings from MIT-Arrhythmia database were 99.34% and 99.31%, respectively. Similarly these parameters of the premature ventricular contraction detection were 87.5% and 91.67%, respectively. The time between alarm and the onset of ventricular fibrillation was measured on ECG recordings where premature ventricular contractions were found to lead to ventricular fibrillation. The remote monitor was able to successfully identify the onset on ventricular fibrillation. Early detection could contribute to better response to an emergency intervention. HRV indices sensitive to the differences between normal and subjects with congestive heart failure were monitored in real-time. They were heart rate, statistical index RMSSD, total spectral power, high frequency power and the ratio of low frequency to high frequency power (LFP:HFP). The effectiveness of HRV indices was tested on an ECG recording of a sleep study subject, who experienced cardiac arrhythmia. Cyclic changes observed in total spectral power prior to onset of cardiac arrhythmia could be attributed to REM sleep cycles. No other conclusive change in HRV indices was observed. The monitor's usefulness in predicting long-term prognosis of post-MI subjects was tested on ECG recordings from two subjects made immediately after conclusion of cardiac arrhythmia and during a follow-up visit. Both showed higher RMSSD, total spectral power and LFP:HFP ratio. Personalizing the monitor for each patient further improves its accuracy in measurement of various parameters

Optimal analog wavelet bases construction using hybrid optimization algorithm

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming (SQP) is proposed. CPSO is an improved PSO in which the saw tooth chaotic map is used to raise its global search ability. CPSO is a global optimizer to search the estimates of the global solution, while the SQP is employed for the local search and refining the estimates. Benefiting from good global search ability of CPSO and powerful local search ability of SQP, a high-precision global optimum in this problem can be gained. Finally, a series of optimal analog wavelet bases are constructed using the hybrid algorithm. The proposed method is tested for various wavelet bases and the improved performance is compared with previous works.Peer reviewedFinal Published versio

An ECG-on-Chip with QRS Detection & Lossless Compression for Low Power Wireless Sensors

IEEE Transactions on Circuits and Systems II: Express BriefsPP991-

VLSI Implementation of a Demand mode Dual Chamber Rate Responsive Cardiac Pacemaker

This project is aimed to design a dual chamber rate responsive cardiac pacemaker, implement it in VLSI and improvise on it for real time safety critical environments. A state machine approach has been followed to achieve the desired purpose. The heart of the pacemaker system rests in the pulse generator which forms the major portion of the project. It has been developed using VHDL and implemented in hardware using FPGA. In the FSM, first an input event is detected. Once this input is detected a timer is set for approximately 0.8 sec, which will be the time between heartbeats, thus giving us 72 heartbeats per minute. Once the timer expires we check to see if a new event is detected. If one is detected we repeat the process of detection and waiting. If one has not been received we need to stimulate the heart and then repeat the process of detection and waiting. The code has been optimized and modified for different pacemaker modes.Adequate effort has been put in for designing a sensing circuit and other peripherals like memory, data compression techniques and remote monitoring equipment,culminating in suggestions for improvement in respective areas. It closes with pacemaker testing for real life applications and scope for further work in the field

Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach

INTRODUCTION Heart function can be impaired by rhythm disturbances (cardiac arrhythmia), illustrated by electrocardiogram (ECG) recordings. Computerised arrhythmia diagnosis is well established for ECG’s but less for intracardiac electrophysiological (EP) testing. Accurate diagnosis is pre-requisite for delivering appropriate treatment to patients however existing algorithms misdiagnose a proportion of arrhythmias. Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies with potential application as a generic rhythm classifier. METHOD An ethically approved prospective clinical study collected clinical history, right atrial and right ventricular intracardiac electrograms, beat-to-beat cardiac stroke volume, body motion and body temperature data during EP studies. An iterative system development life-cycle was used, including knowledge management and classifier development sub-processes. Domain expert knowledge and clinical arrhythmia diagnosis were modelled, synthesised as AI classifiers and used to classify cardiac rhythms. RESULTS Data collected from 65 patients was pre-processed into instances for classifier inputs. Decision tree, naïve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were classified, with the naïve Bayes classifier used to classify 11 rhythms, decision tree 4 rhythms, neural network and support vector machine one each, unclassified instances by the inference engine classifier and final class allocation using decision rule. Production system showed overall correct clasification rate 0.960; error 0.040; mean sensitivity 0.855; mean specificity 0.977; mean κ 0.767; mean positive predictive value 0.792; mean negative predictive value 0.975; mean Pearson’s phi 0.787, with P 0.9 for sinus node dysfunction and atrio-ventricular nodal/ junctional tachycardias. Temperature, accelerometry and QT interval were assessed as features by a comparison of algorithm performances with each feature removed and found not to affect classification performance. An evaluation showed 10 beat analysis performed better than 5 beat analysis. CONCLUSIONS Modelling of the clinical diagnosis process produced an AI based mixture-of-experts multi-classifier system, which accurately diagnosed different 18 cardiac rhythms. The naïve Bayes classifier performed best and classified 11 rhythms. Features for clinical symptoms and predisposing factors, atrial electrogram morphology and changes in stroke volume were found to influence rhythm classification. High performances encourage further development and potential future improvements include: a larger sample dataset; inclusion of His and coronary sinus electrograms; data mining for unknown features with significant influence on diagnosis; binary classification. The aim to classify rhythm using artificial intelligence suitable for use during EP studies was satisfied and the research hypothesis that it outperformed current algorithms was accepted. The system was likely to be able to accept updates but needs conversion as a precursor to use in a live clinical environment

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals