OBJECTIVE: An autoencoder-based framework that simultaneously reconstruct and classify biomedical signals is proposed. Previous work has treated reconstruction and classification as separate problems. This is the first study that proposes a combined framework to address the issue in a holistic fashion. METHODS: For telemonitoring purposes, reconstruction techniques of biomedical signals are largely based on compressed sensing (CS); these are "designed" techniques where the reconstruction formulation is based on some "assumption" regarding the signal. In this study, we propose a new paradigm for reconstruction-the reconstruction is "learned," using an autoencoder; it does not require any assumption regarding the signal as long as there is sufficiently large training data. But since the final goal is to analyze/classify the signal, the system can also learn a linear classification map that is added inside the autoencoder. The ensuing optimization problem is solved using the Split Bregman technique. RESULTS: Experiments were carried out on reconstructing and classifying electrocardiogram (ECG) (arrhythmia classification) and EEG (seizure classification) signals. CONCLUSION: Our proposed tool is capable of operating in a semi-supervised fashion. We show that our proposed method is better in reconstruction and more than an order magnitude faster than CS based methods; it is capable of real-time operation. Our method also yields better results than recently proposed classification methods. SIGNIFICANCE: This is the first study offering an alternative to CS-based reconstruction. It also shows that the representation learning approach can yield better results than traditional methods that use hand-crafted features for signal analysis.
OBJECTIVE: An autoencoder-based framework that simultaneously reconstruct and classify biomedical signals is proposed. Previous work has treated reconstruction and classification as separate problems. This is the first study that proposes a combined framework to address the issue in a holistic fashion. METHODS: For telemonitoring purposes, reconstruction techniques of biomedical signals are largely based on compressed sensing (CS); these are "designed" techniques where the reconstruction formulation is based on some "assumption" regarding the signal. In this study, we propose a new paradigm for reconstruction-the reconstruction is "learned," using an autoencoder; it does not require any assumption regarding the signal as long as there is sufficiently large training data. But since the final goal is to analyze/classify the signal, the system can also learn a linear classification map that is added inside the autoencoder. The ensuing optimization problem is solved using the Split Bregman technique. RESULTS: Experiments were carried out on reconstructing and classifying electrocardiogram (ECG) (arrhythmia classification) and EEG (seizure classification) signals. CONCLUSION: Our proposed tool is capable of operating in a semi-supervised fashion. We show that our proposed method is better in reconstruction and more than an order magnitude faster than CS based methods; it is capable of real-time operation. Our method also yields better results than recently proposed classification methods. SIGNIFICANCE: This is the first study offering an alternative to CS-based reconstruction. It also shows that the representation learning approach can yield better results than traditional methods that use hand-crafted features for signal analysis.
Authors: Sani Saminu; Guizhi Xu; Zhang Shuai; Isselmou Abd El Kader; Adamu Halilu Jabire; Yusuf Kola Ahmed; Ibrahim Abdullahi Karaye; Isah Salim Ahmad Journal: Brain Sci Date: 2021-05-20