Prediction of Cardiovascular Diseases by Integrating Electrocardiogram (ECG) and Phonocardiogram (PCG) Multi-Modal Features using Hidden Semi Morkov Model

Because the health care field generates a large amount of data, we must employ modern ways to handle this data in order to give effective outcomes and make successful decisions based on data. Heart diseases are the major cause of mortality worldwide, accounting for 1/3th of all fatalities. Cardiovascular disease detection can be accomplished by the detection of disturbance in cardiac signals, one of which is known as phonocardiography. The aim of this project is for using machine learning to categorize cardiac illness based on electrocardiogram (ECG) and phonocardiogram (PCG) readings. The investigation began with signal preprocessing, which included cutting and normalizing the signal, and was accompanied by a continuous wavelet transformation utilizing a mother wavelet analytic morlet. The results of the decomposition are shown using a scalogram, and the outcomes are predicted using the Hidden semi morkov model (HSMM). In the first phase, we submit the dataset file and choose an algorithm to run on the chosen dataset. The accuracy of each selected method is then predicted, along with a graph, and a modal is built for the one with the max frequency by training the dataset to it. In the following step, input for each cardiac parameter is provided, and the sick stage of the heart is predicted based on the modal created. We then take measures based on the patient's condition. When compared to current approaches, the suggested HSMM has 0.952 sensitivity, 0.92 specificity, 0.94 F-score, 0.91 ACC, and 0.96 AUC

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Department of Electrical EngineeringBlood Pressure is the most important physiological signal because it is highly associated with various cardiovascular diseases, and it is the basic index to monitor these kinds of diseases. Among various blood pressure measurement methods, Vascular Transit Time (VTT)-based blood pressure estimation method utilizes Photoplethysmogram (PPG), which is the physiological signal from blood volume changes using light, and Phonocardiogram (PCG), which is the cardiac signal corresponding to heart beat sound. In order to continuous blood pressure monitoring in our daily life, real-time VTT monitoring system with PPG and PCG integrated circuit would be excellent solution. The requirements of this system include low-power consumption for long-term operation with a small-sized battery, low-noise characteristic for accurate signal acquisition, and unobstructed measurement position in order to avoid cumbersome. Also, VTT is defined as the time interval between the PCG peak and the PPG peak, peaks of the two signals are needed in VTT calculation. This master???s thesis proposes a readout integrated circuit (ROIC) which achieves low-power consumption, low-noise performance, and easy signal processing. PPG ROIC consists of an LED driver that can control the intensity of the LED and a Light-to-Digital Converter (LDC) operating with a dual-slope mechanism, which converts the photodiode current from a reflected light to digital data. Since the LDC directly converts light into digital, it does not require an additional ADC and suitable for low-power applications. During dual-slope operation, the noise performance is limited by the main integrator and comparator. For low-noise operation, the noise of the core amplifier of the main integrator is reduced by the chopper-stabilization technique, and the quantization noise of the comparator is shaped through a noise-shaping loop. In the noise-shaping loop, the practical noise-shaping performance is reduced due to the charge sharing problem that occurs during the operation of residue voltage save. To solve this problem, an improved noise-shaping loop is proposed. To calculate the VTT, the PCG peak and PPG peak are determined through the digital signal processing in the microcontroller unit (MCU). However, the process of determining the peak only through the shape of the signal waveform alone requires significant processing burden and high-power consumption in the MCU. Especially, for PCG signals, it is very challenging to distinguish different peaks of similar amplitude. Thus, the PCG S1 peak and PCG S2 peak are distinguished respectively using the proposed dual peak detector consisting of a parallel envelope detector and a sort algorithm block. The system for monitoring blood pressure on the chest is implemented with the proposed PPG and PCG integrated circuits. The prototype operates using a battery, and can measure a small chest PPG signal and obtain a PCG signal without using a stethoscope. Through this, it achieves a miniaturized size and convenient usability that can be implemented in wearable devices. The measurement results can be checked in PC MATLAB program using Bluetooth. Furthermore, the proposed system supports not only VTT-based blood pressure monitoring mode but also digital stethoscope mode and heart rate monitoring mode. For verification, a comparison of the blood pressure measurement results with a commercial blood pressure gauge is conducted, and it is confirmed that the proposed system is excellent in blood pressure estimation.ope

Development and Evaluation of an Impedance Cardiographic System to Measure Cardiac Output and Other Cardiac Parameters, 1 July 1968 - 30 June 1969

Impedance cardiographic system to measure cardiac output and cardiovascular function