In current healthcare systems, patients use various types of medical Internet of Things devices for monitoring their health conditions The collected information (personal health records) will be sent back to hospitals for diagnosis and quick responses However, severe security and privacy leakages with regard to data privacy and identity authentication are incurred because the monitored health data contains sensitive information Therefore, the data should be well protected from unauthorized entities Unfortunately, traditional cryptographic approaches or password-based mechanisms cannot fulfill the privacy and security demands in health monitoring due to their low efficiency and knowledge-based property Biometric authentication overcomes these deficiencies and successfully verifies the inherent characteristics of humans Among all biometrics, the electrocardiogram (ECG) signal is the most suitable one due to its medical properties However, the security and privacy objectives of ECG-based authentication usually fail in practice due to the noise interferences in the collected ECG data and the privacy breach of the ECG database In this paper, we propose a practical scheme that can reliably authenticate patients with noisy ECG signals and provide differentially private protection simultaneously The effectiveness and efficiency of our scheme are thoroughly analyzed and evaluated over online datasets We also conduct a pilot study on human subjects experiencing different exercise levels to validate our scheme

https://par.nsf.gov/servlets/purl/10108265

Practical Privacy-Preserving ECG-Based Authentication for IoT-Based Healthcare

Computer-aided detection and diagnosis in ECG signals for heart diseases are gaining increasing attention. However, developing and selecting the highly performing diagnostic model suitable for clinical implications is still challenging. In this paper, we proposed a combined network of convolutional neural network (CNN) and Recurrent Neural Network (RNN), designed for the classification of ECG heart signals for diagnostic purpose. The proposed network consists of 2 convolutional layers with 5 × 5 kernels and ReLU activations, followed by 4 residual blocks, 2 bidirectional long short-term memory (biLSTM) layers, as well as 2 fully connected layers. Each residual block involved the structure of a Squeeze-and-Excitation Network (SENet) with lightweight property to recalibrate the feature map of the network. The last dense layer has 5 outputs, equivalent to the classes considered: non-ectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. To train and evaluate the combined CNN and RNN, we transferred the knowledge acquired on beat classification tasks in 2017 PhysioNet/CinC Challenge to that in PhysionNet's MIT-BIH dataset. The developed network achieved a recognition sensitivity of 95.90%, accuracy = 95.90% and specificity = 96.34% with classification time of single sample = 6.23 s in detecting 5 ECG classes. A comparative analysis proved the high performance of the proposed combined CNN and RNN against previous methods, demonstrating the potential of our proposed network in the analysis of beat patterns. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate cardiac health with the highest precision.

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Interpretation of Electrocardiogram (ECG) Rhythm by Combined CNN and BiLSTM

eHealth systems generate from the integration of information and communication technologies with traditional healthcare systems. They have widely replaced paper-based systems due to their prominent features of convenience and accuracy. However, eHealth systems also face many challenges, such as the privacy and security concerns over patients' identities and their personal health records (PHRs). Traditional cryptographic approaches are only capable of verifying ``what you possess" or ``what you remember" with the help of trust authorities. As a result, they are not suitable for medical applications and cannot handle above concerns effectively. Using biometrics can verify ``who you are" due to permanence, distinctiveness, and undeniability properties of biometrics. It outstands conventional authentication and encryption approaches in eHealth systems. A promising one among all is the ECG (ElectroCardioGram) signal, which is easier to implement than other biometrics. Unfortunately, most of existing works do not take the nonuniformity of ECG signals into consideration. Besides, they do not protect ECG signals well despite their sensitivity. Hence, we propose a robust and reusable authentication and encryption scheme based on ECG signals for eHealth systems. Our scheme can authenticate patients' identities and protect their PHRs, enable the reuse of the same ECG signal, and preserve the privacy of ECG signals. Theoretical and empirical evaluations demonstrate the security, effectiveness, and efficiency of the proposed scheme.

A Robust and Reusable ECG-Based Authentication and Data Encryption Scheme for eHealth Systems

This paper presents a suitable and efficient implementation of a feature extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG) signals, for detection and classification of four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial Fibrillation (AF) and differentiating them from the normal heart beat by using pan Tompkins RR detection followed by feature extraction for classification purpose .The paper also presents a new approach towards signal classification using the existing neural networks classifiers.

Implementation of Neural Network and feature extraction to classify ECG signals

This paper presents an efficient approach for distinguishing ECG signals based on certain diseases by implementing Pan Tompkins algorithm and neural networks. Pan Tompkins algorithm is used for feature extraction on electrocardiography (ECG) signals, while neural networks help in detection and classification of the signal into four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long-Term Atrial Fibrillation (AF) and normal heart beat. The paper also presents a new approach towards signal classification using the existing neural networks classifiers.

Implementation of Neural Network and Feature Extraction to Classify ECG Signals

The greatest challenge faced during the process of diagnosis of cardiovascular diseases is the accurate analyses of the Electrocardiogram (ECG). Many researches are being done to classify and analyze the ECG signals automatically. In this paper, a novel method for the Auto analysis of the ECG signals using MATLAB is proposed and implemented. In this method, the raw ECG data obtained from the patient goes through a process of Wavelet Packet Decomposition (WPD) followed by Feature extraction. The classification is further done using Artificial Neural Network (ANN). This method, succeeding in differentiating the Abnormal ECG signals from the Normal signals, is proved to be a novel method for Auto analysis of ECG signals.

Sainath S Nair

Papers

Auto analysis of ECG signals using artificial neural network