Heart murmurs, resulting from turbulent blood flow within the cardiac structure, represent some of the initial acoustic manifestations of potential underlying cardiovascular anomalies, such as arrhythmias. This research presents a deep learning framework aimed at the early detection of potential cardiac anomalies through the analysis of murmur patterns in phonocardiogram (PCG) signals. Our methodology employs a spectro-temporal feature fusion technique that integrates Mel spectrograms, Mel Frequency Cepstral Coefficients (MFCCs), Root Mean Square (RMS) energy, and Power Spectral Density (PSD) representations. The features are derived from segmented 5-second phonocardiogram (PCG) windows and subsequently input into a two-dimensional convolutional neural network (CNN) for the purpose of classification. In order to mitigate class imbalance and enhance generalization, We employ data augmentation techniques, including pitch moving and noise injection. The model under consideration has undergone training and evaluation utilizing a carefully selected subset of the CirCor DigiScope dataset. The experimental findings indicate a robust performance, with a classification accuracy recorded at 92.40% and a cross-entropy loss measured at 0.2242. The results indicate that an analysis of PCG signals informed by murmurs may function as an effective non-invasive method for the early screening of conditions that may include arrhythmias, particularly in clinical environments with limited resources.

Postoperative atrial fibrillation (POAF) is a leading cause of stroke and mortality after cardiac surgery, motivating continuous long-term ECG monitoring.This paper presents Cardiac-Net, a wearable ECG system (ESP32-S3/AD8232 BLE device with a Flutter mobile gateway; 128 Hz nominal/85 Hz effective BLE sampling rate, 12.6 ms mean latency, zero over-the-air loss of transmitted packets, 3.11 mJ per transmitted sample) combined with a hybrid 1D CNN–Transformer classifier and a proposed cloud-assisted monitoring architecture; the cloud inference and monitoring-dashboard layer is a design-level proposal only and has not been implemented. Bench signal fidelity, validated against a BIOPAC MP36 reference using a Fluke ProSim 8 simulator across five noise conditions, yields a Pearson Correlation Coefficient of 0.9222 and BPM errors below 0.2% under clean conditions, within the 5% heart-rate-error threshold specified by IEC 60601-2-47. The classifier was trained and evaluated on the Long-Term Atrial Fibrillation Database (84 recordings) using a record-wise (patient-wise) data split with verified zero overlap between training, validation, and test recordings; 5-fold record-wise cross-validation spanning all 84 recordings yields AUC $=0.953\pm 0.016$, F1 $=0.894\pm 0.030$, precision $=0.880\pm 0.051$, and recall $=0.915\pm 0.073$; re-evaluating at the device’s actual 85 Hz effective throughput confirms comparable discrimination (AUC $=0.946\pm 0.010$) alongside a real increase in false-alarm burden. A clinical evaluation protocol beyond segment-level accuracy shows that most AF episodes are detected (episode-level sensitivity $0.839\pm 0.222$ by fold; per-patient median 1.000, IQR [0.925, 1.000]), but this is not uniform across patients: 7.2% (6/83) fall below chance-level detection. Combined with a false-alarm burden of $86\pm 38$ alerts per patient-day and a prevalence-adjusted positive predictive value of only 0.28–0.44 at 5–10% assumed AF prevalence, alarm calibration and patient-level reliability both remain barriers to unsupervised clinical deployment. Zero-shot evaluation on the independent MIT-BIH Atrial Fibrillation Database, with no fine-tuning, yields an ensemble AUC of 0.834, indicating generalization beyond the training distribution alongside a measurable domain gap. Post-hoc Grad-CAM and Integrated Gradients attributions are consistent with physiologically grounded decision-making, concentrating on RR-interval irregularity and QRS morphology. Cardiac-Net thus contributes a validated wearable acquisition layer and a rigorously, record-wise cross-validated detection model, together with an honest characterization of the false-alarm and calibration challenges that remain before postoperative clinical deployment.