ECG data for deep transfer learning

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks, AlexNet and GoogLeNet, as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using 8 volunteers. The signals are pre-processed using elliptical filter for signal noises like baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECG with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental data set was augmented by adding two different publicly available data sets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids "reinventing the wheel", but also presents a lightweight solution to otherwise computationally heavy problems.

跌倒因其对人体生理与心理均会造成严重后果，已成为一个突出的公共健康问题。跌倒检测与预防是当前研究的关键领域，该技术可帮助老年人减少对护理人员的依赖，使其能够更自主地生活与行动。本文提出了一种全新的研究思路：独立使用心电图（electrocardiogram, ECG）信号开展跌倒检测与活动分类任务。所提算法采用预训练卷积神经网络AlexNet与GoogLeNet作为分类器，基于ECG信号实现跌倒与非跌倒场景的区分。本研究招募8名志愿者，采集了跌倒与非跌倒场景下的ECG信号数据。针对基线漂移、电力线干扰等信号噪声，所采集的ECG信号均通过椭圆滤波器完成预处理。为提取特征，研究人员对滤波后的ECG信号施加连续小波变换，得到其时频表征——尺度图（scalogram）。将所得尺度图作为神经网络的输入，首个模型的验证准确率达到98.08%。该训练完成的模型可区分跌倒活动与非跌倒活动的ECG信号，整体准确率达98.02%。为验证所提算法的鲁棒性，研究团队通过引入两份公开数据集扩充了本实验的原始数据集。第二个模型可实现跌倒活动、日常活动与非活动三类场景的分类，准确率达98.44%。上述模型均采用迁移学习技术，将真实图像领域的预训练模型迁移至医学图像领域完成训练。相较于传统深度学习方法，迁移学习不仅避免了"重复造轮子"的冗余工作，还为原本计算复杂度极高的问题提供了轻量化解决方案。