criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01) ''' 模型训练 ''' EPOCHS = 5 for epoch in range(EPOCHS): running_loss = 0 for i, data in enumerate(train_loader): input
The purpose of this chapter is to look at the normal parameters that should be measured systematically. Later chapters discuss the meaning behind values that fall outside this 'normal' range.doi:10.1007/978-1-4471-4962-0_4Alan DaviesAlwyn Scott...
(self.model.parameters(), lr=args.lr, weight_decay=args.weight_decay) else: raise Exception("optimizer not implement") if args.lr_scheduler == 'step': steps = [int(step) for step in args.steps.split(',')] self.lr_scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, steps, ...
The internal validation dataset was used to tune the abovementioned hyperparameters. Training process For training, ECG-AIO uses the Adam optimizer27, with default parameters β1 = 0.9 and β2 = 0.999, along with momentum and weight decay regularization. Binary cross-entropy is employed...
The entire measurement process of P-wave parameters was conducted by the same physician throughout the study to ensure consistency. The definition, normal range values, measurement and calculation methods of P-wave parameters was detailed in Supplemental information. Catheter ablation surgical scheme ...
Orthogonal experiments are used to select hyper-parameters. In the evaluation model stage, we use ensemble learning based on a voting strategy to obtain classification performance. Materials and methods Problem definition This paper aims to realize the automatic classification of normal rhythm and 8 car...
Luengo-Fernandez, Burns, Rayner, & Townsend). The traditional CVD diagnosis paradigm is based on individual patient’smedical historyand clinical examinations. These results are interpreted according to a set of the quantitative medical parameters to classify the patients based on the taxonomy of medic...
The defined methodology and parameters, described in this paragraph, are performed over a wide range of continuous-time sampled ECG signals covering many wavelet families. Fig. 7 shows the block diagram of the compression study to evaluate wavelet-based compressibility of LC-ADC outputs. The compres...
it can be deduced that a cardiovascular disease is causing the anomaly (classification of the signal as either normal or pathological). Common uses of the ECG range from diagnosis of chest pain, tachycardia, bradycardia, hypertension, hypotension, myocardial injury, rheumatic heart disease, and more...
7. Repeat this for all parameters you want to change. Note: If you change the minute first, you can only change the hour directly after that, not the date. If you also want to change the date, you need to step through the sequence twice. 8. Press the clock set button (repeatedly)...