A common heart condition that plagues small dogs can be picked up by AI, experts have found. Mitral valve disease regularly affects breeds such as King Charles spaniels, miniature poodles, Pomeranians and chihuahuas. It occurs when one of the heart's valves becomes distorted and leaky. It can progress to become fatal if not treated early on. A research team, led by the University of Cambridge, adapted an algorithm originally designed for humans and found it could automatically detect and grade heart murmurs in dogs - one of the main indicators of the disease.

Acoustic signals are crucial for health monitoring, particularly heart sounds which provide essential data like heart rate and detect cardiac anomalies such as murmurs. This study utilizes a publicly available phonocardiogram (PCG) dataset to estimate heart rate using model-driven methods and extends the best-performing model to a multi-task learning (MTL) framework for simultaneous heart rate estimation and murmur detection. Heart rate estimates are derived using a sliding window technique on heart sound snippets, analyzed with a combination of acoustic features (Mel spectrogram, cepstral coefficients, power spectral density, root mean square energy). Our findings indicate that a 2D convolutional neural network (\textbf{\texttt{2dCNN}}) is most effective for heart rate estimation, achieving a mean absolute error (MAE) of 1.312 bpm. We systematically investigate the impact of different feature combinations and find that utilizing all four features yields the best results. The MTL model (\textbf{\texttt{2dCNN-MTL}}) achieves accuracy over 95% in murmur detection, surpassing existing models, while maintaining an MAE of 1.636 bpm in heart rate estimation, satisfying the requirements stated by Association for the Advancement of Medical Instrumentation (AAMI).

Objective: Heart murmurs are abnormal sounds caused by turbulent blood flow within the heart. Several diagnostic methods are available to detect heart murmurs and their severity, such as cardiac auscultation, echocardiography, phonocardiogram (PCG), etc. However, these methods have limitations, including extensive training and experience among healthcare providers, cost and accessibility of echocardiography, as well as noise interference and PCG data processing. This study aims to develop a novel end-to-end real-time heart murmur detection approach using traditional and depthwise separable convolutional networks. Methods: Continuous wavelet transform (CWT) was applied to extract meaningful features from the PCG data. The proposed network has three parts: the Squeeze net, the Bottleneck, and the Expansion net. The Squeeze net generates a compressed data representation, whereas the Bottleneck layer reduces computational complexity using a depthwise-separable convolutional network. The Expansion net is responsible for up-sampling the compressed data to a higher dimension, capturing tiny details of the representative data. Results: For evaluation, we used four publicly available datasets and achieved state-of-the-art performance in all datasets. Furthermore, we tested our proposed network on two resource-constrained devices: a Raspberry PI and an Android device, stripping it down into a tiny machine learning model (TinyML), achieving a maximum of 99.70%. Conclusion: The proposed model offers a deep learning framework for real-time accurate heart murmur detection within limited resources. Significance: It will significantly result in more accessible and practical medical services and reduced diagnosis time to assist medical professionals. The code is publicly available at TBA.

A heart murmur is an atypical sound produced by the flow of blood through the heart. It can be a sign of a serious heart condition, so detecting heart murmurs is critical for identifying and managing cardiovascular diseases. However, current methods for identifying murmurous heart sounds do not fully utilize the valuable insights that can be gained by exploring intrinsic properties of heart sound signals. To address this issue, this study proposes a new discriminatory set of multiscale features based on the self-similarity and complexity properties of heart sounds, as derived in the wavelet domain. Self-similarity is characterized by assessing fractal behaviors, while complexity is explored by calculating wavelet entropy. We evaluated the diagnostic performance of these proposed features for detecting murmurs using a set of standard classifiers. When applied to a publicly available heart sound dataset, our proposed wavelet-based multiscale features achieved comparable performance to existing methods with fewer features. This suggests that self-similarity and complexity properties in heart sounds could be potential biomarkers for improving the accuracy of murmur detection.

This work leverages deep learning (DL) techniques in order to do automatic and accurate heart murmur detection from phonocardiogram (PCG) recordings. Two public PCG datasets (CirCor Digiscope 2022 dataset and PCG 2016 dataset) from Physionet online database are utilized to train and test three custom neural networks (NN): a 1D convolutional neural network (CNN), a long short-term memory (LSTM) recurrent neural network (RNN), and a convolutional RNN (C-RNN). Under our proposed method, we first do pre-processing on both datasets in order to prepare the data for the NNs. Key pre-processing steps include the following: denoising, segmentation, re-labeling of noise-only segments, data normalization, and time-frequency analysis of the PCG segments using wavelet scattering transform. To evaluate the performance of the three NNs we have implemented, we conduct four experiments, first three using PCG 2022 dataset, and fourth using PCG 2016 dataset. It turns out that our custom 1D-CNN outperforms other two NNs (LSTM- RNN and C-RNN) as well as the state-of-the-art. Specifically, for experiment E1 (murmur detection using original PCG 2022 dataset), our 1D-CNN model achieves an accuracy of 82.28%, weighted accuracy of 83.81%, F1-score of 65.79%, and and area under receive operating charactertic (AUROC) curve of 90.79%. For experiment E2 (mumur detection using PCG 2022 dataset with unknown class removed), our 1D-CNN model achieves an accuracy of 87.05%, F1-score of 87.72%, and AUROC of 94.4%. For experiment E3 (murmur detection using PCG 2022 dataset with re-labeling of segments), our 1D-CNN model achieves an accuracy of 82.86%, weighted accuracy of 86.30%, F1-score of 81.87%, and AUROC of 93.45%. For experiment E4 (abnormal PCG detection using PCG 2016 dataset), our 1D-CNN model achieves an accuracy of 96.30%, F1-score of 96.29% and AUROC of 98.17%.

Pulmonary hypertension is a severe condition that occurs when the pressure in the vessels that carry blood from the heart to the lungs is higher than normal, causing undo stress on the heart. PH affects up to 1% of the global population and is a marker of poor health outcomes.¹ PH can cause premature disability, heart failure, and death. Unfortunately, delays of over two years frequently occur between the onset of symptoms and diagnosis of severe kinds of PH. The gold standards for diagnosing PH are echocardiography and right heart catheterization, which are costly, invasive, and require a heart specialist.

A research study being led by Royal Papworth Hospital and the University of Cambridge is hoping to use artificial intelligence to help diagnose heart valve diseases earlier. Valvular heart disease (VHD) affects nearly two million people in the UK with this number expected to double by 2040. About half of those affected by VHD are unaware of their condition, because symptoms often do not develop until the disease has become severe. Cardiovascular Acoustics and an Intelligent Stethoscope (CAIS) is a clinical study aimed at creating a first-of-its-kind screening tool which could be used to diagnose valve disease before symptoms emerge. Almost 1,200 patients with suspected heart valve disease or congenital heart disease have so far signed up to the study across five NHS hospital sites.

Bluhm Cardiovascular Institute at Northwestern Medicine announced this week that it has been doing new artificial intelligence work in an effort to improve the efficacy and accuracy of its cardiac screening. Clinicians there are using a cardiac monitoring platform from Eko, studying how its AI-enabled digital stethoscopes can interpret heart sounds to help screen for heart murmurs and valvular damage. They depend on a "highly trained musical ear that can separate subtle abnormalities from normal sounds with cardiologist-level precision," according to Northwestern researchers. The idea with the Eko stethoscopes is that AI and machine learning can combine the data from tens of thousands of heart sound patterns to help clinicians better assess what sounds are normal and what's not. "One of the biggest problems in healthcare is that general practitioners so often miss heart murmurs that if found earlier would allow patients to get treatment before problems arise," said Connor Landgraf, CEO of Eko, in a statement.