Intricating cardiac complexities are the primary factor associated with healthcare costs and the highest cause of death rate in the world. However, preventive measures like the early detection of cardiac anomalies can prevent severe cardiovascular arrests of varying complexities and can impose a substantial impact on healthcare cost. Encountering such scenarios usually the electrocardiogram (ECG or EKG) is the first diagnostic choice of a medical practitioner or clinical staff to measure the electrical and muscular fitness of an individual heart. This paper presents a system which is capable of reading the recorded ECG and predict the cardiac anomalies without the intervention of a human expert. The paper purpose an algorithm which read and perform analysis on electrocardiogram datasets. The proposed architecture uses the Discrete Wavelet Transform (DWT) at first place to perform preprocessing of ECG data followed by undecimated Wavelet transform (UWT) to extract nine relevant features which are of high interest to a cardiologist. The probabilistic mode named Bayesian Network Classifier is trained using the extracted nine parameters on UCL arrhythmia dataset. The proposed system classifies a recorded heartbeat into four classes using Bayesian Network classifier and Tukey's box analysis. The four classes for the prediction of a heartbeat are (a) Normal Beat, (b) Premature Ventricular Contraction (PVC) (c) Premature Atrial Contraction (PAC) and (d) Myocardial Infarction. The results of experimental setup depict that the proposed system has achieved an average accuracy of 96.6 for PAC 92.8 for MI and 87 for PVC, with an average error rate of 3.3 for PAC, 6 for MI and 12.5 for PVC on real electrocardiogram datasets including Physionet and European ST-T Database (EDB).
Lately, a very few approaches have been coined to discourse the gaps and limitations of automatic detection of anomalies and generating alarms for irregular rhythms in respect of WBANs, and real-time ECG recording and monitoring remotely. HeartSaver system is such a kind of system projected in @cite_21 with similar intentions. The system is capable of analyzing the ECG in real-time and can detect cardiac pathologies which are atrioventricular block, atrial fibrillation (A-fib) and myocardial infarction. The author in @cite_27 introduced a mobile application which uses the mobile camera for heart attack detection by placing the index figure on camera. The proposed approach works only with Heart Rate by calculating the blood peaks. The purposed approach is unable to detect CVD because CVD detection involves complex features representation and analysis of the ECG signal.
@cite_27 @cite_21
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Heart attack is a global leading cause of death for both gender and the occurrence is not always known to us. Usually Heart Rate Calculation has traditionally been conducted using specialized hardware or device. It used most commonly in the form of pulse oximeters or Electrocardiogram devices. though these devices have higher method and they are reliable to normal user. However, these devices require users to perform their process. In this paper, we propose a system capable of estimating the heart beat rate using just a camera from a commercially available s mart phone and also using a mobile stethoscope to record heart sound for detecting the occurrence of heart attack and also some ot her heart related disease. Fuzzy Logic is used here, which is a part of Data Mining, the expert problem solution for human illness. In general, case people could not u nderstand whenever they face this problem and this is the main cause of death. Our research is about to determine this problem earlier to reduce the death rate of heart attack. The advantage of this method is that the user does not need specialized hardware and he she can take a measurement in virtual ly any place under almost any circumstances. In addition, the measurement can be used as a tool for health coaching applications or effective telecare services aimed in enhancing the user's well being. A mobile medical device, dubbed HeartSaver, is developed for real-time monitoring of a patient's electrocardiogram (ECG) and automatic detection of several cardiac pathologies, including atrial fibrillation, myocardial infarction and atrio-ventricular block. HeartSaver is based on adroit integration of four different modern technologies: electronics, wireless communication, computer, and information technologies in the service of medicine. The physical device consists of four modules: sensor and ECG processing unit, a microcontroller, a link between the microcontroller and the cell phone, and mobile software associated with the system. HeartSaver includes automated cardiac pathology detection algorithms. These algorithms are simple enough to be implemented on a low-cost, limited-power microcontroller but powerful enough to detect the relevant cardiac pathologies. When an abnormality is detected, the microcontroller sends a signal to a cell phone. This operation triggers an application software on the cell phone that sends a text message transmitting information about patient's physiological condition and location promptly to a physician or a guardian. HeartSaver can be used by millions of cardiac patients with the potential to transform the cardiac diagnosis, care, and treatment and save thousands of lives.