Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.ope

Noise reduction method for the heart sound records from digital stethoscope

In recent years, digital instruments have been widely used in the medical area with the rapid development of digital technology. The digital stethoscope, which converts the acoustic sound waves in to electrical signals and then amplifies them, is gradually replacing the conventional acoustic stethoscope with the advantage of additional usage such as restoring, replaying and processing the signals for optimal listening. As the sounds are transmitted in to electrical form, they can be recorded for further signal processing. One of the major problems with recording heart sounds is noise corruption. Although there are many solutions available to noise reduction problems, it was found that most of them are based on the assumption that the noise is an additive white noise [1]. More research is required to find different de-noising techniques based on the specific noise present. Therefore, this study is motivated to answer the research question: ‘How might the noise be reduced from the heart sound records collected from digital stethoscope with suitable noise reduction method’. This research question is divided into three sub-questions, including the identification of the noise spectrum, the design of noise reduction method and the assessment of the method. In the identification stage, five main kinds of noise were chosen and their characteristics and spectrums were discussed. Compared with different kinds of adaptive filters, the suitable noise reduction filter for this study was confirmed. To assess the effect of the method, 68 pieces of sound resources were collected for the experiment. These sounds were selected based on the noise they contain. A special noise reduction method was developed for the noise. This method was tested and assessed with those sound samples by two factors: the noise level and the noise kind. The results of the experiment showed the effect of the noise reduction method for each kind of noise. The outcomes indicated that this method was suitable for heart sound noise reduction. The findings of this study, including the analysis of noise level and noise kind, indicated and concluded that the chosen method for heart sound noise reduction performed well. This is perhaps the first attempt to understand and assess the noise reduction method with classified heart sound signals which are collected from the real healthcare environment. This noise reduction method may provide a de-noising solution for the specific noise present in heart sound

A cost-effective portable telemedicine kit for use in Developing countries

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.Includes bibliographical references (p. 95-96).Telemedicine is currently being used to bridge the physical distance between patients in remote areas and medical specialists around the world. Developing countries have had little experience or success with telemedicine, in part because of the prohibitively expensive equipment and connectivity costs involved. Developing countries require low-cost, sustainable telemedicine solutions for the local delivery of primary healthcare and efficient access to medical expertise when needed. A low-cost (approximately $8,000 in small quantities) portable telemedicine kit was designed and built to address these needs. The kit was developed as part of the Little Intelligent Communities (LINCOS) project, which is bringing satellite telecommunications, education and telemedicine services to underserved areas of Latin America and the Caribbean. This is accomplished through the use of modified ISO shipping containers that become 'digital town centers.' The telemedicine kit consists of a durable case that houses a portable computer and several medical peripherals: a digital stethoscope, an ECG recorder and a medical imaging system. The kit allows a health practitioner in a remote area to capture patient data in the form of audio, video, and images in a asynchronous fashion and forward them over the Internet to a doctor for a diagnosis. This document addresses various aspects related to the implementation of a low cost telemedicine kit. It also explores some of the technologies that will enable the creation of new types of telemedicine devices in the future, not only for remote diagnostic applications, but also for home health monitoring. A wireless transceiver board was also designed and built so that it could be embedded into consumer medical and electronic devices in a general fashion. It allows the devices to communicate wirelessly with a base station either for home health monitoring applications, or for a cordless version of the portable telemedicine kit.by Ari T. Adler.S.M

Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure

Heart failure (HF) is an international public health priority and a focus of the NHS Long Term Plan. There is a particular need in primary care for screening and early detection of heart failure with reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic, and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable, accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and 80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was independent of operator, and usability of the tool extended to patients being able to self-examine. Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (£2,500 per patient). This underpins a health economic model for the deployment of AI-ECG across primary care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient self-administered AI-ECG screening programme. I propose approaches to mitigating any potential harms, towards preserving and promoting trust, patient engagement, and public health. Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health economic, and ethical considerations can directly serve patients and health systems by blueprinting best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction needed to realise the full potential of such technologies.Open Acces

PCG and ECG Portable Acquisition System

Heart disease is the leading cause of death in the world, victimizing human beings in all age groups and from different geographic areas. Through the evolution of auscultation technology, the identification and analysis of heartbeats make it possible to prevent and treat heart pathologies with greater success. In this project, a portable, low-power system was developed that allows the acquisition of heart sound signals simultaneously in four different auscultation zones, as well as the acquisition of an electrical signal, subsequently conditioning and processing the different signals. The system also allows the transmission of signals in real-time via Bluetooth Low Energy to a mobile device where they can be recorded for future analysis. The portable system records four phonocardiographs located at four auscultation areas, namely the aortic valve, the mitral valve, the pulmonary valve and the tricuspid valve. These sound signals are converted to analog electrical signals that will be amplified, filtered and converted into digital signals. There is also a two-electrode electrocardiograph in this system, which is also amplified and filtered before being converted to a digital signal and transmitted via wireless communication. The entire system is powered by a battery with a charge voltage of 4.2 V, which allows it to be charged through the USB interface.As doenças cardíacas são a principal causa de morte no mundo, vitimizando seres humanos em todas as faixas etárias e de diferentes àreas geográficas. Através da evolução da tecnologia na auscultação, a identificação e análise dos batimentos cardíacos permitem prevenir e tratar patologias do coração com maior sucesso. Neste projeto foi desenvolvido um sistema portátil de baixa potência que permite a aquisição dos sinais sonoros do coração simultaneamente em quatro diferentes zonas de auscultação, assim como a aquisição de um sinal elétrico, fazendo posteriormente o condicionamento e processamento dos diferentes sinais. O sistema permite ainda a transmissão em tempo-real dos sinais via Bluetooth Low Energy para um dispositivo móvel onde podem gravados para futura análise. O sistema portátil regista quatro fonocardiogramas localizados em quatro áreas de auscultação, nomeadamente na válvula aórtica, na válvula mitral, na válvula pulmónica e na válvula tricúspida. Estes sinais sonoros são convertidos para sinais elétricos analógicos que serão amplificados, filtrados e convertidos de novo para sinais digitais. O sistema regista também o electrocardiograma através de dois elétrodos, que é também amplificado e filtrado antes de ser convertido em sinal digital e transmitido via comunicação sem fios. Todo o sistema é alimentado por uma bateria com tensão de carga 4.2 V, que permite ser carregada através de interface USB

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormity has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to do, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies

Aerospace Medicine and Biology - A continuing bibliography with indexes

Annotated bibliography and indexes on Aerospace Medicine and Biology - Dec. 196