The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. The visualizer can start reading while the signal preprocessor is writing into it. Reading and writing into the same file poses a challenge. The signal preprocessor writes into the file while the visualizer reads from it. This user-defined file holds raw signal information as a buffer for the visualizer. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. In the first stream, the feature extractor receives the signals using stdin. The signal preprocessor writes the sample frames into two streams to facilitate these modules. Feature extraction is performed sequentially on each channel. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. The system begins processing the EEG signal by applying a TCP montage. The online system accepts streamed EEG data sampled at 250 Hz as input. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The system then displays the EEG signal and the decisions simultaneously using a visualization module. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The feature extractor generates LFCC features in real time from the streaming EEG signal. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) features from each EEG channel separately. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. An overview of the system is shown in Figure 1. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system into a low latency real-time or online seizure detection system. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor and Persyst 14. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency. Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy.
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