2024
A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device
Sangha V, Dhingra L, Khera R. A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device. European Heart Journal 2024, 45: ehae666.3437. DOI: 10.1093/eurheartj/ehae666.3437.Peer-Reviewed Original ResearchDeep learning modelsPDF outputArtificial intelligenceLearning modelsConvolutional neural networkPortable devicesPortable ECG deviceMultilabel modelEfficientNet-B3Neural networkECG outputWearable ECGHeld-out subsetCurrent algorithmsSingle-lead ECGSynthetic ECGECG deviceWearableClinical ECGClinical labelsAlgorithmCommercial devicesDetection of conductivityNo current algorithmPDF
2023
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Mortazavi B, Coppi A, Brandt C, Krumholz H, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. Npj Digital Medicine 2023, 6: 124. PMID: 37433874, PMCID: PMC10336107, DOI: 10.1038/s41746-023-00869-w.Peer-Reviewed Original ResearchArtificial intelligenceRandom Gaussian noiseNoisy electrocardiogramGaussian noiseElectrocardiogram (ECGWearable devicesSingle-lead electrocardiogramPortable devicesSNRWearableNoiseDevice noiseRepositoryAI-based screeningIntelligenceDetectionDevicesNoise sourcesVentricular systolic dysfunctionModelElectrocardiogramSingle-lead electrocardiographyTraining