Heart and lung sounds are crucial for healthcare monitoring. Recent improvements in stethoscope technology have made it possible to capture patient sounds with enhanced precision. In this dataset, we used a digital stethoscope to capture both heart and lung sounds, including individual and mixed recordings. To our knowledge, this is the first dataset to offer both separate and mixed cardiorespiratory sounds. The recordings were collected from a clinical manikin, a patient simulator designed to replicate human physiological conditions, generating clean heart and lung sounds at different body locations. This dataset includes both normal sounds and various abnormalities (i.e., murmur, atrial fibrillation, tachycardia, atrioventricular block, third and fourth heart sound, wheezing, crackles, rhonchi, pleural rub, and gurgling sounds). The dataset includes audio recordings of chest examinations performed at different anatomical locations, as determined by specialist nurses. Each recording has been enhanced using frequency filters to highlight specific sound types. This dataset is useful for applications in artificial intelligence, such as automated cardiopulmonary disease detection, sound classification, unsupervised separation techniques, and deep learning algorithms related to audio signal processing.

Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively.

For Dr Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest – using lung ultrasound images – and DeepBreath – using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.

For Dr. Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr. Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest--using lung ultrasound images--and DeepBreath--using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.

Screening for significant aortic stenosis was fast and effective through the assessment of phonocardiograms by a digital stethoscope and machine learning, according to results presented at the American Society of Echocardiography Scientific Sessions. "A machine-learning algorithm trained on heart sounds can rapidly and accurately detect a murmur in patients with clinically significant aortic stenosis," Steve Pham, MD, vice president of clinical and research affairs at Eko Devices, told Cardiology Today. "Front-line clinicians may be able to use Eko stethoscopes (Eko CORE) with this algorithm to refer patients for echocardiography to confirm aortic stenosis." Brent E. White, MD, of the Bluhm Cardiovascular Institute at Northwestern Memorial Hospital in Chicago, and colleagues analyzed 639 recordings from 161 patients who were undergoing transthoracic echocardiography. The 15-second phonocardiogram recordings were obtained from the digital stethoscope, which is wirelessly paired with a mobile app (Eko Mobile).

Pneumonia, an acute respiratory condition which affects the lungs, kills millions of people around the world each year. This includes 16 percent of all children who die under the age of five. It's particularly devastating in parts of the world without the necessary trained doctors and required medical equipment, such as X-ray machines, to treat it effectively. Researchers from Johns Hopkins University think so. Spinning off to form the startup Sonavi Labs, they have developed an updated version of this core piece of medical equipment which has remained largely unchanged since the 1800s, boasting some smart, cutting-edge additions. This includes smart noise-filtering technology for enhancing the sound quality of chest readings.