AI detects congestive heart failure with one heartbeat
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A new study has reported success in identifying severe heart failure in 100% of cases using a single heartbeat recording from an electrocardiogram (ECG). Medically, the condition called congestive heart failure (CHF) refers to a chronic loss of pumping power in the heart which is progressive.

It is fairly common, causes significant illness and disability, and pushes up the costs of medical care. It affects about 26 million people around the world, and is more common in the elderly. It causes a considerable number of deaths, with about 40% mortality among the most severe cases. Even with treatment, relapses are common. It costs about 2% to 3% of total healthcare budgets.

How was the current study done?

The current study used an artificial intelligence (AI) approach called Convolutional Neural Networks (CNN) which makes use of neural networks arranged in layers of increasing complexity, similar to the visual pathway. These are able to detect data patterns and structures at extremely high efficiency, and have been used to perform speech recognition, arrhythmia detection and general time series classification.

The data for the control group was taken from the MIT-BIH Normal Sinus Rhythm Database containing 18 ECG recordings of healthy subjects, and for the CHF group from the BIDMC Congestive Heart Failure Database, containing 15 ECG recordings of patients with severe CHF. One heartbeat was randomly selected from each 5s segment. The dataset was split into three parts for training, validation and testing, and individual heartbeats were used in only one subset at a time.

The heartbeats were evaluated both individually, and on 5 minutes of ECG excerpts by a so-called majority voting scheme, using the number of heartbeats that were classified as normal or as CHF and assigning a final class.

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