Heart sounds typically consist of two regular sounds, known as S1 and S2, for every heartbeat. The normal blood flow inside the heart is mainly laminar and therefore silent; but when the blood flow becomes turbulent it causes vibration of surrounding tissue and hence the blood flow is noisy and perceivable, causing an audible murmur. Murmurs are often indicative of pathological changes, but they overlap with the cardiac beat and cannot be easily separated by the human ear using a traditional stethoscope.
Eko's algorithms are designed to differentiate between healthy heart sounds and those containing heart murmurs using a deep neural network model trained on multiple datasets of heart sounds. By accurately detecting the location of the S1 and S2 sounds, our algorithms can automatically specify whether a murmur is systolic or diastolic.
When analyzing a single lead ECG, Eko's algorithms can detect the presence of atrial fibrillation and normal sinus rhythm using state-of-the-art machine learning techniques. The algorithms use an ensemble of deep neural network models that have been trained and validated across multiple datasets to ensure they perform accurately and robustly on real-world data. Furthermore, the models are capable of detecting comorbidities including tachycardia, bradycardia, and premature ventricular contractions (PVCs).
Eko has also partnered with the Mayo Clinic to develop a first-of-its kind heart failure screening algorithm that uses DUO's single lead ECG to detect asymptomatic left ventricular dysfunction via detecting low Ejection Fraction.
By screening with Eko’s ECG algorithms, patients with undiagnosed rhythm abnormalities or heart failure could be detected earlier and start preventive therapy earlier meaning future adverse events, including strokes, might be avoided.
The ECG and stethoscope give two different snapshots of heart function and from their simultaneous collection Eko’s algorithms can detect unique characteristics that are often unavailable during routine care, such as the Electromechanical Activation Time.
The Electromechanical Activation Time (EMAT) is a measurement of the time interval between the electrical activation of the heart as measured by the onset of the Q wave of the ECG and the mechanical closure of the mitral and tricuspid valves as measured by the peak of the S1 heart sound. It has been widely studied as one of the systolic time intervals which measure the electromechanical desynchronization of the heart and is commonly used as a metric to aid in detection and monitoring of congestive heart failure.
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