Heart sound monitoring sys

Cardiovascular disease (CVD) is among the leading life threatening ailments [1] [2].Under normal circumstances, a cardiac examination utilizing electrocardiogram appliances or tools is proposed for a person stricken with a heart disorder. The logging of irregular heart behaviour and morphology is frequently achieved through an electrocardiogram (ECG) produced by an electrocardiographic appliance for tracing cardiac activity. For the most part, gauging of this activity is achieved through a non-invasive procedure i.e. through skin electrodes. Taking into consideration the ECG and heart sound together with clinical indications, the cardiologist arrives at a diagnosis on the condition of the patient's heart. This paper focuses on the concerns stated above and utilizes the signal processing theory to pave the way for better heart auscultation performance by GPs. The objective is to take note of heart sounds in correspondence to the valves as these sounds are a source of critical information. Comparative investigations regarding MFCC features with varying numbers of HMM states and varying numbers of Gaussian mixtures were carried out for the purpose of determining the impact of these features on the classification implementation at the sites of heart sound auscultation. We employ new strategy to evaluate and denoise the heart and ecg signal with a specific end goal to address specific issues

Heart Murmur Diagnostic System (HMDS)

Many heart diseases cause changes in heart sounds and additional murmurs before other signs and symptoms appear. Heart sound auscultation is the primary test conducted by general practitioner (GP) . A heart murmur is an abnormal, extra sound during the heartbeat cycle made by blood flowing through the heart and its valves. Murmurs are characterized by their timing, intensity, pitch, shape and location. Timing refers to whether the murmur occurs during systole, diastole or is continuous throughout the cardiac cycle. This paper describes the diagnosis of heart sounds and heart murmurs using stethoscope based on MFCC-HMM. Diagnosing heart sounds depends on the experience and training of the General Practitioner. Echocardiography is the gold standard alternative for diagnosing heart diseases. Even though it provides a more definitive diagnosis in this respect, it is expensive and not widely available throughout Malaysia especially in local hospitals. For the classification based on the HMM, the continuous cyclic heart sound signal needs to be automatically segmented to obtain isolated cycles of the signal. The ECG signal characteristic of the R to R point is used to determine every one minute cyclesof both ECG and heart sound data. The experiment includes varying the number of states and a number of mixtures. In the classification experiments, the proposed method performed successfully with an accuracy of about 98.9% with 5 states,16 gaussion model

A brief review of computation techniques for ECG signal analysis

Automatic detection of life-threatening cardiac arrhythmias has been a subject of interest for many decades. The automatic ECG signal analysis methods are mainly aiming for the interpretation of long-term ECG recordings. In fact, the experienced cardiologists perform the ECG analysis using a strip of ECG graph paper in an event-by-event manner. This manual interpretation becomes more difficult, time-consuming, and more tedious when dealing with long-term ECG recordings. Rather, an automatic computerized ECG analysis system will provide valuable assistance to the cardiologists to deliver fast or remote medical advice and diagnosis to the patient. However, achieving accurate automated arrhythmia diagnosis is a challenging task that has to account for all the ECG characteristics and processing steps. Detecting the P wave, QRS complex, and T wave is crucial to perform automatic analysis of EEG signals. Most of the research in this area uses the QRS complex as it is the easiest symbol to detect in the first stage. The QRS complex represents ventricular depolarization and consists of three consequences waves. However, the main challenge in any algorithm design is the large variation of QRS, P, and T waveform, leading to failure for each method. The QRS complex may only occupy R waves QR (no R), QR (no S), S (no Q), or RSR, depending on the ECG lead. Variations from the normal electrical patterns can indicate damage to the heart, and these variations are manifested as heart attack or heart disease. This paper will discuss the most recent and relevant methods related to each sub-stage, maintaining the related literature to the scope of ECG research

Classification of ECG ventricular beats assisted by Gaussian parameters’ dictionary

Automatic processing and diagnosis of electrocardiogram (ECG) signals remain a very challenging problem, especially with the growth of advanced monitoring technologies. A particular task in ECG processing that has received tremendous attention is to detect and identify pathological heartbeats, e.g., those caused by premature ventricular contraction (PVC). This paper aims to build on the existing methods of heartbeat classification and introduce a new approach to detect ventricular beats using a dictionary of Gaussian-based parameters that model ECG signals. The proposed approach relies on new techniques to segment the stream of ECG signals and automatically cluster the beats for each patient. Two benchmark datasets have been used to evaluate the classification performance, namely, the QTDB and MIT-BIH Arrhythmia databases, based on a single lead short ECG segment. Using the QTDB database, the method achieved the average accuracies of 99.3% ± 0.7 and 99.4% ± 0.6% for lead-1 and lead-2, respectively. On the other hand, identifying ventricular beats in the MIT-BIH Arrhythmia dataset resulted in a sensitivity of 82.8%, a positive predictivity of 62.0%, and F1 score of 70.9%. For non-ventricular beats, the method achieved a sensitivity of 96.0%, a positive predictivity of 98.6%, and F1 score of 97.3%. The proposed technique represents an improvement in the field of ventricular beat classification compared with the conventional methods

Enhanced signal processing using modified cyclic shift tree denoising

The cortical pyramidal neurons in the cerebral cortex, which are positioned perpendicularly to the brain’s surface, are assumed to be the primary source of the electroencephalogram (EEG) reading. The EEG reading generated by the brainstem in response to auditory impulses is known as the Auditory Brainstem Response (ABR). The identification of wave V in ABR is now regarded as the most efficient method for audiology testing. The ABR signal is modest in amplitude and is lost in the background noise. The traditional approach of retrieving the underlying wave V, which employs an averaging methodology, necessitates more attempts. This results in a protracted length of screening time, which causes the subject discomfort. For the detection of wave V, this paper uses Kalman filtering and Cyclic Shift Tree Denoising (CSTD). In state space form, we applied Markov process modeling of ABR dynamics. The Kalman filter, which is optimum in the mean-square sense, is used to estimate the clean ABRs. To save time and effort, discrete wavelet transform (DWT) coefficients are employed as features instead of filtering the raw ABR signal. The results show that even with a smaller number of epochs, the wave is still visible and the morphology of the ABR signal is preserved