diff --git a/README.md b/README.md index 2afba1c..883063c 100644 --- a/README.md +++ b/README.md @@ -55,29 +55,26 @@ ECG实质上是一种微弱的生物电信号，也因此，其对于噪声的 3. 开发者个人PC配置为i7-11800H，RTX3070Laptop，32G DDR4，具备针对本课题的算法运行能力。 4. 开发者可通过浙江图书馆、Google学术等平台，获取到相应的文献以针对本课题进行进一步的知识储备。 -## 3.3 主要参考文献 +## 3.3 毕业论文参考文献 -[1] 《中国心血管健康与疾病报告2021》要点解读 [J]. 中国心血管杂志, 2022, 27(04): 305-18. - -[2] SAMOL A, BISCHOF K, LUANI B, et al. Single-Lead ECG Recordings Including Einthoven and Wilson Leads by a Smartwatch: A New Era of Patient Directed Early ECG Differential Diagnosis of Cardiac Diseases? [J]. Sensors, 2019, 19(20): 4377. - -[3] 张宁宁, 马金刚, 樊昭磊, et al. 基于机器学习的心电自动诊断算法综述 [J]. 医疗卫生装备, 2022, 43(05): 92-7. - -[4] 刘奇. 基于集成学习算法的ECG身份识别 [D]; 吉林大学, 2019. - -[5] 欧阳波. 基于小波分析的ECG信号处理技术研究 [D]; 湖南大学, 2014. - -[6] LOGESH R, SUBRAMANIYASWAMY V, MALATHI D, et al. Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method [J]. Neural Computing and Applications, 2020, 32(7): 2141-64. - -[7] 李伟康. 基于混合深度学习算法的ECG心电信号分类研究 [D]; 江苏科技大学, 2022. - -[8] 胡军锋, 郑彬. 基于深度学习的ECG/PPG血压测量方法 [J]. 生物医学工程研究, 2022, 41(1): 46-54. - -[9] ZHU J, LV J, KONG D. CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive [J]. Entropy, 2022, 24(4): 471-. - -[10] PETMEZAS G, HARIS K, STEFANOPOULOS L, et al. Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets [J]. Biomedical Signal Processing and Control, 2021, 63: 102194. - -[11] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述 [J]. 计算机学报, 2017, 40(6): 1229-51. + [1] 《中国心血管健康与疾病报告2021》要点解读 [J]. 中国心血管杂志, 2022, 27(04): 305-18. + [2] SAMOL A, BISCHOF K, LUANI B, et al. Single-Lead ECG Recordings Including Einthoven and Wilson Leads by a Smartwatch: A New Era of Patient Directed Early ECG Differential Diagnosis of Cardiac Diseases? [J]. Sensors, 2019, 19(20): 4377. + [3] 刘奇. 基于集成学习算法的ECG身份识别 [D]; 吉林大学, 2019. + [4] 欧阳波. 基于小波分析的ECG信号处理技术研究 [D]; 湖南大学, 2014. + [5] LOGESH R, SUBRAMANIYASWAMY V, MALATHI D, et al. Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method [J]. Neural Computing and Applications, 2020, 32(7): 2141-64. + [6] 李伟康. 基于混合深度学习算法的ECG心电信号分类研究 [D]; 江苏科技大学, 2022. + [7] 胡军锋, 郑彬. 基于深度学习的ECG/PPG血压测量方法 [J]. 生物医学工程研究, 2022, 41(1): 46-54. + [8] ZHU J, LV J, KONG D. CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive [J]. Entropy, 2022, 24(4): 471-. + [9] PETMEZAS G, HARIS K, STEFANOPOULOS L, et al. Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets [J]. Biomedical Signal Processing and Control, 2021, 63: 102194. + [10] GOLDBERGER A, AMARAL, L., GLASS, L., HAUSDORFF, J., IVANOV, P.C., MARK, R., MIETUS, J.E., MOODY, G.B., PENG, C.K. AND STANLEY, H.E., 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. [J]. Circulation [Online], 2000, (101 (23)): e215–e20. + [11] 贺京生. 基于小波变换的十二导联心电信号分析研究 [D]; 南昌大学, 2016. + [12] HUBEL D H, WIESEL T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex [J]. The Journal of Physiology, 1962, 160(1): 106-54. + [13] FUKUSHIMA K. Cognitron: A self-organizing multilayered neural network [J]. Biological Cybernetics, 1975, 20(3-4): 121-36. + [14] FUKUSHIMA K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position [J]. Biological Cybernetics, 1980, 36(4): 193-202. + [15] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-324. + [16] TOMPOLLARD. MIT-LCP/wfdb-python:Native Python WFDB package [Z]. + [17] HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Comput, 1997, 9(8): 1735-80. + [18] BUBECK S E, CHANDRASEKARAN V, ELDAN R, et al. Sparks of Artificial General Intelligence: Early experiments with GPT-4 [J]. arXiv pre-print server, 2023. # 4 代码文件说明 ## 4.1 _main_tensorflow_oneLayerCNN.py @@ -199,4 +196,5 @@ ECG实质上是一种微弱的生物电信号，也因此，其对于噪声的 ## 6.3 网络结构的构建 本部分不做展开描述。您有想法可发邮件至this_weiyuqing@foxmail.com探讨。 - \ No newline at end of file + +# 7 代码运行说明 \ No newline at end of file diff --git a/_mian_pytorch.py b/_mian_pytorch.py deleted file mode 100644 index 0a8e320..0000000 --- a/_mian_pytorch.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np -import pywt -import wfdb -import torch - -from ECG_read_weiyuqing_without_wfdb import ECGDATAPATH - - -def denoise(date): - # Wavelet db5 level9 - coeffs = pywt.wavedec(data=date, wavelet='db5', level=9) - cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs - # noice del - threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1)))) - # take cD 1,2 turn 0 to denoise - cD1.fill(0) - cD2.fill(0) - for i in range(1, len(coeffs) - 2): - coeffs[i] = pywt.threshold(coeffs[i], threshold) - # get signal that before wavelet - rdata = pywt.waverec(coeffs=coeffs, wavelet='db5') - return rdata - - -# get ECG data,date type and denoise -# this function without return -# use append take return data to pass data -def getDataSet(number, X_data, Y_data): - ecgClassSet = ['N', 'A', 'V', 'l', 'R'] - print('get' + number + ' ECG data') - record = wfdb.rdrecord(ECGDATAPATH + number, channel_names=['MLII']) - data = record.p_signal.flatten() - # use def denoise - rdata = denoise(date=data) - - # get type of ECG data - annotation = wfdb.rdann(ECGDATAPATH + number, 'atr') - Rlocation = annotation.sample # annotation.sample:get R - Rclass = annotation.symbol - - # delete stable date near R - start = 10 - end = 5 - i = start - j = len(annotation.symbol) - end - - # get NAVLR type data - while i < j: - try: - lable = ecgClassSet.index(Rclass[i]) - x_train = rdata[Rlocation[i] - 99:Rlocation[i] + 201] - X_data.append(x_train) - Y_data.append(lable) - i += 1 - except ValueError: - i += 1 - return - - -# RATIO=The ratio of the training set to the test set，usually is 0.3 -def loadData(RATIO): - numberSet = [ - '100', '101', '103', '105', '106', '107', '108', '109', '111', '112', '113', '114', '115', '116', '117', '119', - '121', '122', '123', '124', '200', '201', '202', '203', '205', '208', '210', '212', '213', '214', '215', '217', - '219', '220', '221', '222', '223', '228', '230', '231', '232', '233', '234' - ] - dataSet = [] - labelSet = [] - for n in numberSet: - getDataSet(n, dataSet, labelSet) - - dataSet = np.array(dataSet).reshape(-1, 300) - labelSet = np.array(labelSet).reshape(-1, 1) - train_ds = np.hstack((dataSet, labelSet)) - np.random.shuffle(train_ds) - - X = train_ds[:, :300].reshape(-1, 300, 1) - Y = train_ds[:, 300] - # cut test and train data from ECG data - shuffle_index = np.random.permutation(len(X)) - test_length = int(RATIO * len(shuffle_index)) - test_index = shuffle_index[:test_length] - train_index = shuffle_index[test_index:] - X_test, Y_test = X[test_index:], Y[test_index] - X_train, Y_train = X[train_index], Y[train_index] - return X_train, Y_test, X_test, Y_train - - -class CNN_model_level_one(torch.nn.Module): - def __init__(self): - super().__init__() - self.conv1 = torch.nn.Conv1d(in_channels=1,out_channels=1,) \ No newline at end of file