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.
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.
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。
In order to solve the problem that the early onset of paroxysmal atrial fibrillation is very short and difficult to detect, a detection algorithm based on sparse coding of Riemannian manifolds is proposed. The proposed method takes into account that the nonlinear manifold geometry is closer to the real feature space structure, and the computational covariance matrix is used to characterize the heart rate variability (RR interval variation), so that the data is in the Riemannian manifold space. Sparse coding is applied to the manifold, and each covariance matrix is represented as a sparse linear combination of Riemann dictionary atoms. The sparse reconstruction loss is defined by the affine invariant Riemannian metric, and the Riemann dictionary is learned by iterative method. Compared with the existing methods, this method used shorter heart rate variability signal, the calculation was simple and had no dependence on the parameters, and the better prediction accuracy was obtained. The final classification on MIT-BIH AF database resulted in a sensitivity of 99.34%, a specificity of 95.41% and an accuracy of 97.45%. At the same time, a specificity of 95.18% was realized in MIT-BIH NSR database. The high precision paroxysmal atrial fibrillation detection algorithm proposed in this paper has a potential application prospect in the long-term monitoring of wearable devices.
Lorenz plot (LP) method which gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze cardiac arrhythmias, and the morphologies and positions of the extracted attractors may reveal the underlying mechanisms of the onset and termination of arrhythmias. But automatic diagnosis is still impossible because it is lack of the method of extracting attractors by now. We presented here a methodology of attractor extraction and recognition based upon homogeneously statistical properties of the location parameters of scatter points in three dimensional LP (3DLP), which was constructed by three successive RR intervals as X, Y and Z axis in Cartesian coordinate system. Validation experiments were tested in a group of RR-interval time series and tags data with frequent unifocal premature complexes exported from a 24-hour Holter system. The results showed that this method had excellent effective not only on extraction of attractors, but also on automatic recognition of attractors by the location parameters such as the azimuth of the points peak frequency (APF) of eccentric attractors once stereographic projection of 3DLP along the space diagonal. Besides, APF was still a powerful index of differential diagnosis of atrial and ventricular extrasystole. Additional experiments proved that this method was also available on several other arrhythmias. Moreover, there were extremely relevant relationships between 3DLP and two dimensional LPs which indicate any conventional achievement of LPs could be implanted into 3DLP. It would have a broad application prospect to integrate this method into conventional long-time electrocardiogram monitoring and analysis system.
The monitoring of pregnant women is very important. It plays an important role in reducing fetal mortality, ensuring the safety of perinatal mother and fetus, preventing premature delivery and pregnancy accidents. At present, regular examination is the mainstream method for pregnant women's monitoring, but the means of examination out of hospital is scarce, and the equipment of hospital monitoring is expensive and the operation is complex. Using intelligent information technology (such as machine learning algorithm) can analyze the physiological signals of pregnant women, so as to realize the early detection and accident warning for mother and fetus, and achieve the purpose of high-quality monitoring out of hospital. However, at present, there are not enough public research reports related to the intelligent processing methods of out-of-hospital monitoring for pregnant women, so this paper takes the out-of-hospital monitoring for pregnant women as the research background, summarizes the public research reports of intelligent processing methods, analyzes the advantages and disadvantages of the existing research methods, points out the possible problems, and expounds the future development trend, which could provide reference for future related researches.
目的:了解阻塞性睡眠呼吸暂停综合征患者的心率变异改变。方法:对67例睡眠打鼾患者同步进行24小时动态心电图及多导睡眠图监测。根据PSG检测结果分为OSAS组和单纯鼾症组,比较组间低频峰(LF),高频峰(HF),低频峰与高频峰的比值(LF/HF),正常RR间期平均值及其标准差值(SDNN),正常RR间期差值均方根(rMSSD)。结果:OSAS组中,频域分析指标:LF,HF,均低于单纯鼾症组,LF/HF高于对照组,时域分析指标:SDNN,rMSSD均低于对照组。结论:OSAHS患者心率变异性降低。
Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry out accurate and efficient early diagnosis of inferior myocardial infarction. Electrocardiogram is the most sensitive means for early diagnosis of inferior myocardial infarction. This paper proposes a method for detecting inferior myocardial infarction based on densely connected convolutional neural network. The method uses the original electrocardiogram (ECG) signals of serially connected Ⅱ, Ⅲ and aVF leads as the input of the model and extracts the robust features of the ECG signals by using the scale invariance of the convolutional layers. The characteristic transmission of ECG signals is enhanced by the dense connectivity between different layers, so that the network can automatically learn the effective features with strong robustness and high recognition, so as to achieve accurate detection of inferior myocardial infarction. The Physikalisch Technische Bundesanstalt diagnosis public ECG database was used for verification. The accuracy, sensitivity and specificity of the model reached 99.95%, 100% and 99.90%, respectively. The accuracy, sensitivity and specificity of the model are also over 99% even though the noise exists. Based on the results of this study, it is expected that the method can be introduced in the clinical environment to help doctors quickly diagnose inferior myocardial infarction in the future.