China
Africa
Explore chapters and articles related to this topic
Machine Learning for Rural Healthcare
Published in Shikha Agrawal, Manish Gupta, Jitendra Agrawal, Dac-Nhuong Le, Kamlesh Kumar Gupta, Swarm Intelligence and Machine Learning, 2022
Parveen Kumar Lehana, Chaahat, Akshita Abrol, Priti Rajput, Parul
Signal based processing is one of the important research areas. Signals may be of different types like image, sound and electromagnetic. The signals generated from living bodies are classified as biomedical signals and can be processed for a variety of applications including health state estimation [3–10]. The signals may be obtained from different physiological systems in the human body such as the circulatory, muscular and nervous systems. As the heart is an important organ in the human body, the signals recorded around it provide significant information about the functioning of the body. The audio signals in the form of heart sounds, usually called phonocardiogram (PCG) signals, contain enough information about the working of the heart in particular and working of the body in general. Because of the complexity involved in the analysis of the heart sounds, the interpretation of the experts may vary depending upon their skill and expertise. Hence, there is a need for automating the process of diagnosis and decision-making using machine learning algorithms. The researchers have focussed on how Ayurveda, an ancient Indian medical science understands and visualizes the human body. The ayurvedic body is conceptualised as being composed of five constituent parts (Panchamahabhutas: space, air, fire, water and earth) [11–13] and a hierarchical relationship exists between these elements [14, 15] that is represented in Fig. 2.
A Survey of Machine Learning in Healthcare
Published in Mitul Kumar Ahirwal, Narendra D. Londhe, Anil Kumar, Artificial Intelligence Applications for Health Care, 2022
S. Sathyanarayanan, Sanjay Chitnis
Heart Auscultation: Cardiac disorders can be diagnosed using heart auscultation with high accuracy. Cardiac auscultation is a procedure that involves listening and analysing heart sounds using an electronic stethoscope, a device that records heart sounds digitally. The recording is called a phonocardiogram (PCG). Several digital signal processing and ML techniques could be used to study the PCG and diagnose heart disorders. Automated heart sound analysis is called computer-aided auscultation (CAA). CAA is being applied for development of affordable tools for diagnosis.
Time–Frequency Signal Representations for Biomedical Signals
Published in Hualou Liang, Joseph D. Bronzino, Donald R. Peterson, Biosignal Processing, 2012
G. Faye Boudreaux-Bartels, Robin Murray
The recording of heart sounds, or phonocardiogram (PCG) signal, has been analyzed using many time-frequency techniques. Bulgrin et al. (1993) compared the short-time Fourier transform and the WT for the analysis of abnormal PCGs. Picard et al. (1991) analyzed the sounds produced by different prosthetic valves using the spectrogram. The binomial RID, which is a fast approximation to the CWD, was used to analyze the short-time, narrow-bandwidth features of first heart sound in mongrel dogs by Wood et al. (1992).
A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks
Published in Cogent Engineering, 2020
Abdulkader Joukhadar, Louay Chachati, Mohammed Al-Mohammed, Obada Albasha
According to the World Health Organization (WHO) Center, cardiovascular diseases are the number one cause of death globally, taking an estimation of 17.9 million lives each year. Many cardiac diseases, which are valve related, can be easily and less costly detected by the analysis of phonocardiogram (PCG) signal, i.e. heart sound signal. Therefore, an automatic analysis of PCG signal is used to improve the diagnosis of heart valve diseases, especially in rural health-care clinics where neither experienced physicians nor Doppler-echocardiography equipment might exist. The goal of this work is to provide a primary diagnostic tool for heart valve diseases. Thus, in this paper, a Raspberry Pi-based system is proposed for the diagnosis of nine common valvular heart cases. The proposed system is portable, easy to use, and can provide the diagnosis result immediately.
Analysis of the four heart sounds statistical study and spectro-temporal characteristics
Published in Journal of Medical Engineering & Technology, 2020
The processing of the phonocardiogram signal (PCG) in terms of recordings is very important for the diagnosis of various cardiac pathologies. The PCG signal confirms, and above all, refines the auscultation data and provides additional information on sound activities regarding the chronology of pathological signs in the cardiac cycle, by situating them in relation to normal non-pathological sounds of the heart [4–8].