The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screening of family genetic diseases. This research proposed a HCM automatic detection method based on convolution neural network (CNN) model, using single-lead electrocardiogram (ECG) signal as the research object. Firstly, the R-wave peak locations of single-lead ECG signal were determined, followed by the ECG signal segmentation and resample in units of heart beats, then a CNN model was built to automatically extract the deep features in the ECG signal and perform automatic classification and HCM detection. The experimental data is derived from 108 ECG records extracted from three public databases provided by PhysioNet, the database established in this research consists of 14,459 heartbeats, and each heartbeat contains 128 sampling points. The results revealed that the optimized CNN model could effectively detect HCM, the accuracy, sensitivity and specificity were 95.98%, 98.03% and 95.79% respectively. In this research, the deep learning method was introduced for the analysis of single-lead ECG of HCM patients, which could not only overcome the technical limitations of conventional detection methods based on multi-lead ECG, but also has important application value for assisting doctor in fast and convenient large-scale HCM preliminary screening.
Objective To conduct a systematic review on the Electrocardiogram (ECG) changes in the workers exposed to high temperatures by means of meta-analysis.Methods The retrospective cohort studies on the relationship between high temperature and ECG abnormalities published from 1990 to May 2009 were searched in CNKI, VIP, WanFang database and CBM database. The literatures meeting the inclusive criteria were selected, the quality was assessed, the data were extracted, and the meta-analyses were conducted with RevMan 4.2.2 software. Results A total of 20 studies were included. The results of meta-analyses showed: the ECG abnormality rate of the high-temperature group was obviously superior to that of the control group with significant difference (OR=2.76, 95%CI 2.37 to 3.20, Plt;0.000 01). The high-temperature severely affected left ventricular hypertrophy (OR=3.49, 95%CI 2.83 to 4.31, Plt;0.000 01), sinus bradycardia (OR=2.83, 95%CI 2.33 to 3.43, Plt;0.000 01), and changes in ST-T segment (OR=2.63, 95%CI 1.48 to 4.68, P=0.000 10), which indicated that the abnormal changes of ECG, such as left ventricular hypertrophy, sinus tachycardia, sinus bradycardia, and changes in ST-T segment could be the sensitive indexes to monitor cardiovascular disease of workers exposed to high-temperature. Conclusion The incidence of ECG abnormalities caused by high-temperature operation is obviously superior to that of the control group, so it is required to strengthen the health monitoring and labor protection for the workers exposed to high temperature.
目的 探讨冠状动脉钙化检测联合动态心电图对冠心病的诊断价值及临床应用。 方法 对2010年5月－2011年8月住院的108例拟诊冠心病的患者同期进行128排螺旋CT冠状动脉钙化积分检测、动态心电图和冠状动脉造影，对比研究冠状动脉钙化检测联合动态心电图预测冠心病的价值。 结果 冠状动脉钙化阳性预测冠心病的灵敏度、特异度、阳性预测值和阴性预测值分别为75.6%、81.0%、73.9%、82.3%；动态心电图阳性预测冠心病的灵敏度、特异度、阳性预测值和阴性预测值分别为73.3%、76.2%、68.8%、80.0%；冠状动脉钙化检测联合动态心电图的系列实验的特异度和阳性预测值分别达到96.8%和92.9%，平行试验的灵敏度和阴性预测值分别达到93.3%和92.7%，均显著高于单项试验的相应指标（P＜0.05）。 结论 高分辨率螺旋CT冠状动脉钙化检测联合动态心电图显著提高冠心病的诊断价值，可作为老年患者及基层医院冠心病首选的筛选检查。
Electrocardiogram (ECG) is a noninvasive, inexpensive, and convenient test for diagnosing cardiovascular diseases and assessing the risk of cardiovascular events. Although there are clear standardized operations and procedures for ECG examination, the interpretation of ECG by even trained physicians can be biased due to differences in diagnostic experience. In recent years, artificial intelligence has become a powerful tool to automatically analyze medical data by building deep neural network models, and has been widely used in the field of medical image diagnosis such as CT, MRI, ultrasound and ECG. This article mainly introduces the application progress of deep neural network models in ECG diagnosis and prediction of cardiovascular diseases, and discusses its limitations and application prospects.
Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.
Valvular heart disease (VHD) ranks as the third most prevalent cardiovascular disease, following coronary artery disease and hypertension. Severe cases can lead to ventricular hypertrophy or heart failure, highlighting the critical importance of early detection. In recent years, the application of deep learning techniques in the auxiliary diagnosis of VHD has made significant advancements, greatly improving detection accuracy. This review begins by introducing the etiology, pathological mechanisms, and impact of common valvular heart diseases. It then explores the advantages and limitations of using electrocardiographic signals, phonocardiographic signals, and multimodal data in VHD detection. A comparison is made between traditional risk prediction methods and large language models (LLMs) for predicting cardiovascular disease risk, emphasizing the potential of LLMs in risk prediction. Lastly, the current challenges faced by deep learning in this field are discussed, and future research directions are proposed.
Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F1-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Due to the characteristics and environmental factors, electrocardiogram (ECG) signals are usually interfered by noises in the course of signal acquisition, so it is crucial for ECG intelligent analysis to eliminate noises in ECG signals. On the basis of wavelet transform, threshold parameters were improved and a more appropriate threshold expression was proposed. The discrete wavelet coefficients were processed using the improved threshold parameters, the accurate wavelet coefficients without noises were gained through inverse discrete wavelet transform, and then more original signal coefficients could be preserved. MIT-BIH arrythmia database was used to validate the method. Simulation results showed that the improved method could achieve better denoising effect than the traditional ones.
目的:对无明显心血管病(CVD)临床症状者的高甘油三酯(TG)≥1.60 mmol/L低高密度脂蛋白胆固醇(HDL-C)≤1.18 mmol/L伴有活动平板运动试验(TET)心电图(ECG)阳性和TET ECG阴性的缺血性心脏病(IHD)的危险因素进行了对比观察。〖HTH〗方法:〖HT5”SS〗对无明显CVD临床症状的2900例受试者检测TG/HDL-C、其中伴有TET ECG阳性(缺血型ST-T改变)者500例和TET ECG阴性(不伴有缺血型ST-T改变)者2500例进行了5年对比观察, 预测其预后。〖HTH〗结果〖HTSS〗:在 5年随访的观察中高TG(≥1.60 mmol/L)/低HDL-C(≤1.18 mmmol/L)伴有TET ECG阳性者500例的IHD的发生(30例)率为6.00%;IHD死亡(14例)率为2.80%。而高TG/低HDL-C TET ECG 阴性者2500例的IHD发生(25例)率为2.80%, 死亡(8例)率为0.32%, Plt;0.001。表明高TG/低HDL-C伴有TET ECG阳性者是IHD的较大危险因素。结论:高TG/低H DL-C, 伴有TET ECG阳性对IHD者的死亡率的预测有重要意义, 提示二者指标共同作用对IHD者极为不利。
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.