Segmentação de Eletrocardiogramas de Cães usando Redes Neurais Convolucionais

Electrocardiogram exams are essential in the diagnosis of heart diseases in animals. However, manually segmenting the waves that compose an ECG is a laborious task. This work proposes an algorithm based on convolutional neural networks that accurately segments the P, QRS, and T waves in the cardiac complexes of dogs. The algorithm achieved a mean absolute error of less than two samples in 99% of predictions and a false negative rate of under 0.02%. Additionally, this proposed algorithm outperformed a wavelet transform-based method currently used in veterinary software, demonstrating performance that is approximately three times better.