Human studies

The source data used for training DeepBeat comprised a combination of a novel data generated for this study and publicly available data. Pretraining using CDAE was trained with a novel PPG simulated dataset, and DeepBeat was developed using participants from three datasets, two from Stanford hospital, first participants undergoing elective cardioversions and secondly, participants performing elective stress tests. The third dataset is a publicly accessible 2015 IEEE Signal Processing Cup Dataset was used to supplement the Stanford dataset to provide out-of-institution examples. For an additional evaluation, a pulse-oximeter benchmark data and a study from an ambulatory cohort were used to evaluate algorithm performance. The participant demographic summary can be found in Table 1. All studies conducted at Stanford were conducted in accordance with the principles outlined in the Declaration of Helsinki and approved by the Institutional Review Board of Stanford University (protocol ID 35465, Euan Ashley). All participants provided informed consent prior to the initiation of the study.

The simulation of synthetic physiological signals was generated and built upon RRest, a simulation framework29. The simulation framework for synthetic physiological signals was expanded to include a combination of baseline wander and amplitude modulation for simulation of sinus rhythm physiological signals. For simulations of an AF state, a combination of frequency modulation, baseline wander, and amplitude modulation was simulated. Frequency modulation was applied to specifically to mimic the chaotic irregularity of an AF rhythm. This assumption was the foundation for all simulations of AF signals. In addition to the expanded simulation version, an additional noise component was added to the simulated signals based on a Gaussian noise distribution. This provides the capability to simulate high-quality signals in the presence of low noise and low-quality signals in the presence of high noise. We simulated sinus rhythm and AF states under different levels of Gaussian noise to best represent observed real-world scenarios, further details can be found in Supplementary Fig. 1.

The collection of physiological signals before cardioversion were extracted from a wrist-based PPG wearable device worn by participants at Stanford hospital undergoing direct current cardioversion for the treatment of AF. The study included participants with an AF diagnosis who were scheduled for elective cardioversion. We included all adult participants able to provide informed consent and willing to wear the device before and after the CV procedure. We included all participants with an implanted pacemaker or defibrillator and who also had planned or unplanned transesophageal echocardiogram. In total, 132 participants were recruited and monitored; data from 107 were of sufficient duration and quality to be included in this study. The average monitoring time was ~20 min post and 20 min prior to the CV. All physiological signals were sampled at 128 Hz and wirelessly transmitted via Wifi to a cloud-based storage system.

The collection of physiological signals from exercise stress test were extracted from a wrist-based PPG wearable device worn by participants at Stanford hospital who were scheduled for an elective exercise stress test. We included all adult participants who were able to provide informed consent and willing to wear the device during an elective exercise stress test. In total, 42 participants were monitored; data from all 42 participants were included in this study. The average monitoring time was ~45 min. All physiological signals were sampled at 128 Hz and wirelessly transmitted via Wifi to a cloud-based storage system.

The PPG database from the 2015 IEEE Signal Processing Cup30 was included in this study to provide a source of data from healthy non-AF participants. The dataset consists of two channels of PPG signals, three channels of simultaneous acceleration signals, and one channel of simultaneous ECG signal. PPG signals were recorded from a participant’s wrist using PPG sensors built-in a wristband. The acceleration signals were recorded using a tri-axial accelerometer built into the wristband. The ECG signals were recorded using standard ECG sensors located on the chest of participants. All signals were sampled at 125 Hz and wirelessly transmitted via Bluetooth to a local computer.

The pulse-oximeter benchmark dataset was downloaded from the on-line database The dataset consists of individuals randomly selected from a larger collection of physiological signals collected during elective surgery and routine anesthesia for the purpose of development of improved monitoring algorithms in adults and children31. The PPG signals were recorded at 100 Hz with S/5 Collect software (Datex-Ohmeda, Finland)31.

A prospective cohort of 15 participants with paroxysmal AF were recruited prospectively for a free-living ambulatory monitoring for an average of 1 week. Participants wore a wrist-based PPG wearable device together with an ECG reference device. During the monitoring period, participants were asked to continue with their regular daily activities in their normal environment. PPG signals were extracted from the device after study was complete and clinically-annotated ECG rhythm annotations were provided from the reference ECG device.

Data preprocessing

Preprocessing of the simulated physiological signals for CDAE consisted of partitioning the data into training, validation, and test partitions. Simulated physiological PPG signals consisted of 25 s time frames. The collected physiological signals were partitioned into training, validation, and test partitions with no individual overlap between each set. We used overlapping windows for the training set as a data augmentation technique to increase the number of training examples. All signals were standardized to [0, 1] bounds and bandpass filtered and downsampled by a factor of 4. Supplementary Table 1 illustrates the number of signals for each partition from the Stanford cardioversion, exercise stress test, and IEEE signal challenge datasets.

Signal quality assessment

To train a multitask model assessing both signal quality and event detection, signal quality labels were needed for each signal window. Event-detection labels were known, given the datasets and timestamp the signal originated from. To provide a signal quality assessment label for the training set we created an expert scored dataset of PPG signals known as the signal quality assessment dataset. For each time window set considered, 1000 randomly selected windows were scored and partitioned into a train, validate and test sets. Each window was scored according to 1 of 3 categories (excellent, acceptable, and noise) in the concordance of published recommendations for PPG signal quality7. The signal quality classes were based on published standardized criteria (Elgendi quality assessments7). A separate model for QA was trained using the scored dataset as outcomes and used to predict quality labels for the remaining unscored windows considered.


Pretraining was performed using unsupervised pretraining using convolutional denoising autoencoders. Autoencoders are a type of neural network that is composed of two parts, an encoder, and decoder. Given a set of unlabeled training inputs, the encoder is trained to learn a compressed approximation for the identity function so that the decoder can produce output similar to that of the input, using backpropagation. Consider an input (x in Re ^d) being mapped to a hidden compressed representation (y in Re ^d) by the encoder function: Encoder: (y = h_theta (x) = sigma (Wx + b),) where W is the weights matrix, b is the bias array, θ = {W, b}, and σ can be any nonlinear function such as ReLu. The latent representation y is then mapped back into a reconstruction z, with the same shape as input x using a similar mapping: Decoder: (z = sigma (W^prime y + b^prime )). The reconstruction of the autoencoder attempts to learn the function such that (h_theta (x) approx x), to minimize the mean-squared difference (L(x,z) = {sum} {(x – h_theta (x))^2}). Convolutional denoising autoencoders (CDAE) are a stochastic extension to traditional autoencoders explained above. In CDAE, the initial input x is corrupted to (underline x) by a stochastic mapping (underline x = C(underline x |x)), where C is a noise generating function, which partially destroys the input data. The hidden representation y of the kth feature map is represented by (y^k = sigma (W^k ast x + b)), where * denotes the 1D convolutional operation and σ is a nonlinear function. The decoder is denoted by (z approx h_theta (x) = sigma (mathop {sum}nolimits_{i in m} {m^i} ast underline W + b)), where m indicates the group of latent feature maps and (underline W) is the flipped operation over the dimensions of W32. Compared to traditional autoencoders, convolutional autoencoders can utilize the full capability of CNN to exploit structure within the input with weights shared among all input locations to help preserve local spatiality32.

We simulated a training dataset for artifact induced PPG signals and its corresponding clean/target signal. We use convolutional and pooling layers in the encoder, and upsampling and convolutional layers in the decoder. To obtain the optimal weights for W, weights were randomly initiated according to He distribution33 and the gradient calculated by using the chain rule to back-propagate error derivatives through the decoder network and then the encoder network. Using a number of hidden units lower than the inputs forces the autoencoder to learn a compressed approximation. The loss function employed in pretraining was mean-squared error (MSE) and was optimized using a back-propagation algorithm. The input to the CDAE was the simulated signal dataset with a Gaussian noise factor of 0.001, 0.5, 0.25, 0.75, 1, 2, and 5 added to corrupt the simulated signals. The uncorrupted simulated signals are then used as the target for reconstruction. We used three convolution layers and three pooling layers for the encoder segment and three convolution layers and three upsampling layers for the decoder segment of the CDAE, Supplementary Table 3. ReLU was applied as the activation function and Adam34 is used as the optimization method. Each model was trained with MSE loss for 200 epochs with a reduction in learning rate by 0.001 for every 25 epochs if validation loss did not improve. Further results from the CDAE training can be found in the Supplementary Note 3, Supplementary Fig. 2, and Supplementary Table 4.

Transfer learning is an appealing approach for problems where labeled data is acutely scarce35. In general terms, transfer learning refers to the process of first training a base network on a source dataset and task before transferring the learned features (the network’s weights) to a second network which is trained on an external and sometimes related dataset and task. The power of transfer learning is rooted in its ability to deal with domain mismatch. Fine-tuning pretrained weights on the new dataset is implemented by continuing backpropagation. It has been shown that transfer learning reduces the training time by reducing the number of epochs needed for the network to converge on the training set36. We utilize transfer learning here by extracting the encoder weights from the pretrained CDAE and copy the weights to the first three layers of the DeepBeat model architecture. A similar approach has been applied before successfully in related ECG arrhythmia detection22. The motivation behind using CDAE for unsupervised pretraining on simulated physiological signals was to provide the earlier foundational layers of the DeepBeat model the ability to quickly identify learned features that constitute important physiological signal elements.

DeepBeat was trained to classify two tasks through a shared structure. The input is a single physiological PPG signal. The convolutional layers of the first three layers include receptive field maps with initialized filter weights from the pretrained CDAE encoder section. Three additional layers were added after the encoder section, leading to a total of six shared hidden layers. For hidden layers 4–6, leaky rectified linear unit37, batch normalization38, and dropout layers and convolutional parameters were selected through hyperparameter search. Model specification can be found in Supplementary Table 5.

The quality assessment task (QA) and AF event-detection task builds upon the six shared layers branching into two specialized arms, the QA task and event-detection task. The QA arm consists of an additional convolutional layer, rectified linear unit (ReLU), batch normalization, dropout and two dense layers before final softmax activation for classification is used. The event-detection task consisted of three additional convolutional layers, each followed by a ReLU, batch normalization, and dropout layers. Two additional dense layers were added before final softmax activation. For all layers except the pretrained encoder, weights were randomly initiated according to He distribution33. With a given training input, predictions for both tasks, QA and rhythm event detection were estimated and backpropagation was used to update the weights and the corresponding gradients throughout the network. Hyperparameter optimization for the number of layers, activation functions, receptive field map size, convolutional filter length, epochs, and stride was performed using hyperas39. The best performing model was selected by highest F1 score on the validation data. We implemented DeepBeat using Python 3.5 and Keras40 with Tensorflow 1.241. We trained the model on a cluster of 4 NVIDIA P100 GPU nodes.

DeepBeat performance metrics

Classification performance metrics were measured in two ways, per episode and weighted macro-averaged across individuals using the following metrics: sensitivity, specificity, false-positive rate, false-negative rate, and F1 score. While accuracy is classically used for evaluating overall performance, F1 scores are more useful with significant class imbalance, as is the case here. Weighted macro-averaged are reported within the tables.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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