Evaluating canine electrocardiograms (ECG) require skilled veterinarians, but
current availability of veterinary cardiologists for ECG interpretation and
diagnostic support is limited. Developing tools for automated assessment of ECG
sequences can improve veterinary care by providing clinicians real-time results
and decision support tools. We implement a deep convolutional neural network
(CNN) approach for classifying canine electrocardiogram sequences as either
normal or abnormal. ECG records are converted into 8 second Lead II sequences
and classified as either normal (no evidence of cardiac abnormalities) or
abnormal (presence of one or more cardiac abnormalities). For training ECG
sequences are randomly augmented using RandomAugmentECG, a new augmentation
library implemented specifically for this project. Each chunk is then is
converted using a continuous wavelet transform into a 2D scalogram. The 2D
scalogram are then classified as either normal or abnormal by a binary CNN
classifier. Experimental results are validated against three boarded veterinary
cardiologists achieving an AUC-ROC score of 0.9506 on test dataset matching
human level performance. Additionally, we describe model deployment to
Microsoft Azure using an MLOps approach. To our knowledge, this work is one of
the first attempts to implement a deep learning model to automatically classify
ECG sequences for canines.Implementing automated ECG classification will
enhance veterinary care through improved diagnostic performance and increased
clinic efficiency.

使用深度卷积神经网络 (CNN) 方法对犬的心电图序列进行分类研究，以诊断出是否出现心脏异常状况，以提高兽医的诊断性能，从而实现完善的兽医护理。

PulseNet：使用随机增强策略和连续小波变换的深度学习心电信号分类（犬用）

PulseNet: Deep Learning ECG-signal classification using random  augmentation policy and continous wavelet transform for canines

The artificial intelligence (AI) system has achieved expert-level performance
in electrocardiogram (ECG) signal analysis. However, in underdeveloped
countries or regions where the healthcare information system is imperfect, only
paper ECGs can be provided. Analysis of real-world ECG images (photos or scans
of paper ECGs) remains challenging due to complex environments or interference.
In this study, we present an AI system developed to detect and screen cardiac
abnormalities (CAs) from real-world ECG images. The system was evaluated on a
large dataset of 52,357 patients from multiple regions and populations across
the world. On the detection task, the AI system obtained area under the
receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test
1), 0.984 (external test 2), and 0.979 (external test 3), respectively.
Meanwhile, the detection results of AI system showed a strong correlation with
the diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist
2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of
0.894 (hold-out test) and 0.850 (external test). The screening performance of
the AI system was better than that of the cardiologists (AI system (0.846) vs.
cardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the
feasibility of an accurate, objective, easy-to-use, fast, and low-cost AI
system for CA detection and screening. The system has the potential to be used
by healthcare professionals, caregivers, and general users to assess CAs based
on real-world ECG images.

本研究设计了一个 AI 系统，能够在 real-world ECG 图像中检测和筛选心脏异常。该系统的表现优于心脏病学家，在心脏异常筛查方面表现最好。该系统具有准确性，客观性，易于使用，快速，低成本等特点，适用于医护人员、护理者和普通用户评估基于 real-world ECG 图像的 CAs。

使用心电图图像进行心脏异常检测和筛查的人工智能系统

Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images

A large number of people suffer from life-threatening cardiac abnormalities,
and electrocardiogram (ECG) analysis is beneficial to determining whether an
individual is at risk of such abnormalities. Automatic ECG classification
methods, especially the deep learning based ones, have been proposed to detect
cardiac abnormalities using ECG records, showing good potential to improve
clinical diagnosis and help early prevention of cardiovascular diseases.
However, the predictions of the known neural networks still do not
satisfactorily meet the needs of clinicians, and this phenomenon suggests that
some information used in clinical diagnosis may not be well captured and
utilized by these methods. In this paper, we introduce some rules into
convolutional neural networks, which help present clinical knowledge to deep
learning based ECG analysis, in order to improve automated ECG diagnosis
performance. Specifically, we propose a Handcrafted-Rule-enhanced Neural
Network (called HRNN) for ECG classification with standard 12-lead ECG input,
which consists of a rule inference module and a deep learning module.
Experiments on two large-scale public ECG datasets show that our new approach
considerably outperforms existing state-of-the-art methods. Further, our
proposed approach not only can improve the diagnosis performance, but also can
assist in detecting mislabelled ECG samples. Our codes are available at
this https URL.

通过将临床规则引入卷积神经网络以提高自动心电图诊断性能，该论文提出了一种称为 HRNN 的手工规则增强神经网络，并在两个大型公共心电图数据集上进行了实验。