Biblio

We present a scalable architecture based on a trained filter bank for input pre-processing and a recurrent neural network (RNN) for the detection of atrial fibrillation in electrocardiogram (ECG) signals, with the focus on enabling a very efficient hardware implementation as application-specific integrated circuit (ASIC). Our already very efficient base architecture is further improved by replacing the RNN with a delta-encoded gated recurrent unit (GRU) and adding a confidence measure (CM) for terminating the computation as early as possible. With these optimizations, we demonstrate a reduction of the processing load of 58 % on an internal dataset while still achieving near state-of-the-art classification results on the Physionet ECG dataset with only 1202 parameters.

Internet of things (IoT) healthcare devices, like other IoT devices, typically use proprietary protocol communications. Usually, these proprietary protocols are not audited and may present security flaws. Further, new proprietary protocols are desgined in the field of IoT devices, like data-over-sound communications. Data-over-sound is a new method of communication based on audio with increasing popularity due to its low hardware requirements. Only a speaker and a microphone are needed instead of the specific antennas required by Bluetooth or Wi-Fi protocols. In this paper, we analyze, audit and reverse engineer a modern IoT healthcare device used for performing electrocardiograms (ECG). The audited device is currently used in multiple hospitals and allows remote health monitoring of a patient with heart disease. For this auditing, we follow a black-box reverse-engineering approach and used STRIDE threat analysis methodology to assess all possible attacks. Following this methodology, we successfully reverse the proprietary data-over-sound protocol used by the IoT healthcare device and subsequently identified several vulnerabilities associated with the device. These vulnerabilities were analyzed through several experiments to classify and test them. We were able to successfully manipulate ECG results and fake heart illnesses. Furthermore, all attacks identified do not need any patient interaction, being this a transparent process which is difficult to detect. Finally, we suggest several short-term solutions, centred in the device isolation, as well as long-term solutions, centred in involved encryption capabilities.

To share the recorded ECG data with the cardiologist in Golden Hours in an efficient and secured manner via tele-cardiology may save the lives of the population residing in rural areas of a country. This paper proposes an encryption-authentication scheme for secure the ECG data. The main contribution of this work is to generate a one-time padding key and deploying an encryption algorithm in authentication mode to achieve encryption and authentication. This is achieved by a water cycle optimization algorithm that generates a completely random one-time padding key and Triple Data Encryption Standard (3DES) algorithm for encrypting the ECG data. To validate the accuracy of the proposed encryption authentication scheme, experimental results were performed on standard ECG data and various performance parameters were calculated for it. The results show that the proposed algorithm improves security and passes the statistical key generation test.

The latest security methods are based on biometric features. The electrocardiogram is increasingly used in such systems because it provides biometric features that are difficult to falsify. This paper aims to study the use of the electrocardiogram together with the Convolutional Neural Networks, in order to identify the subjects based on the ECG signal and to improve the security. In this study, we used the Fantasia database, available on the PhysioNet platform, which contains 40 ECG recordings. The ECG signal is pre-processed, and then spectrograms are generated for each ECG signal. Spectrograms are applied to the input of several architectures of Convolutional Neural Networks like Inception-v3, Xception, MobileNet and NasNetLarge. An analysis of performance metrics reveals that the subject identification method based on ECG signal and CNNs provides remarkable results. The best accuracy value is 99.5% and is obtained for Inception-v3.

Electrocardiogram (ECG) biometric authentication (EBA) is a promising approach for human identification, particularly in consumer devices, due to the individualized, ubiquitous, and easily identifiable nature of ECG signals. Thus, computing architectures for EBA must be accurate, fast, energy efficient, and secure. In this article, first, we implement an EBA algorithm to achieve 100% accuracy in user authentication. Thereafter, we extensively analyze the algorithm to show the distinct variance in execution requirements and reveal the latency bottleneck across the algorithm's different steps. Based on our analysis, we propose a domain-specific architecture (DSA) to satisfy the execution requirements of the algorithm's different steps and minimize the latency bottleneck. We explore different variations of the DSA, including one that features the added benefit of ensuring constant timing across the different EBA steps, in order to mitigate the vulnerability to timing-based side-channel attacks. Our DSA improves the latency compared to a base ARM-based processor by up to 4.24×, while the constant timing DSA improves the latency by up to 19%. Also, our DSA improves the energy by up to 5.59×, as compared to the base processor.

Security has become the vital component of today's technology. People wish to safeguard their valuable items in bank lockers. With growing technology most of the banks have replaced the manual lockers by digital lockers. Even though there are numerous biometric approaches, these are not robust. In this work we propose a new approach for personal biometric identification based on features extracted from ECG.

The edge and fog computing paradigms enable more responsive and smarter systems without relying on cloud servers for data processing and storage. This reduces network load as well as latency. Nonetheless, the addition of new layers in the network architecture increases the number of security vulnerabilities. In privacy-critical systems, the appearance of new vulnerabilities is more significant. To cope with this issue, we propose and implement an Ethereum Blockchain based architecture with edge artificial intelligence to analyze data at the edge of the network and keep track of the parties that access the results of the analysis, which are stored in distributed databases.

ECG signals are often corrupted by 50 Hz noise, the frequency from the power supply. So it becomes quite necessary to remove Power Line Interference (PLI) from the ECG signal. The reference ECG signal data was taken from the MIT-BIH database. Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI. Later, the comparison was made among the methods, to find the best methodology to denoise the corrupted ECG signal. The parameters that were used for the comparison are Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR). Higher values of SNR & PSNR and lower values of MSE & MAE define the best denoising algorithm.

This study proposes a method for ECG signals quality assessment (SQA) by using temporal feature, and heuristic rule. The ECG signal will be classified as acceptable or unacceptable. Seven types of noise were able to be detected by the prosed method. The noises are: FL, TVN, BW, AB, MA, PLI and AWGN. The proposed method is aimed to have better performance for SQA than classical machine learning method. The experiment is conducted by using 1000 instances ECG signal. The experiment result shows that db8 has the best performance with 0.86, 0.85 and 85.6% on lead-1 signal and 0.69, 0.79, and 74% on lead-5 signal for specificity, sensitivity and accuracy respectively. Compared to the classical machine learning, the proposed heuristic method has same accuracy but has 48% and 31% better specificity for lead-1 and lead-5. It means that the proposed method has far better ability to detect noise.

Wearable Internet-of-Things (WIoT) environments have demonstrated great potential in a broad range of applications in healthcare and well-being. Security is essential for WIoT environments. Lack of security in WIoTs not only harms user privacy, but may also harm the user's safety. Though devices in the WIoT can be attacked in many ways, in this paper we focus on adversaries who mount what we call sensor-hijacking attacks, which prevent the constituent medical devices from accurately collecting and reporting the user's health state (e.g., reporting old or wrong physiological measurements). In this paper we outline some of our experiences in implementing a data-driven security solution for detecting sensor-hijacking attack on a secure wearable internet-of-things (WIoT) base station called the Amulet. Given the limited capabilities (computation, memory, battery power) of the Amulet platform, implementing such a security solution is quite challenging and presents several trade-offs with respect to detection accuracy and resources requirements. We conclude the paper with a list of insights into what capabilities constrained WIoT platforms should provide developers so as to make the inclusion of data-driven security primitives in such systems.

Wearable and mobile medical devices provide efficient, comfortable, and economic health monitoring, having a wide range of applications from daily to clinical scenarios. Health data security becomes a critically important issue. Electrocardiogram (ECG) has proven to be a potential biometric in human recognition over the past decade. Unlike conventional authentication methods using passwords, fingerprints, face, etc., ECG signal can not be simply intercepted, duplicated, and enables continuous identification. However, in many of the studies, algorithms developed are not suitable for practical application, which usually require long ECG data for authentication. In this work, we introduce a two-phase authentication using artificial neural network (NN) models. This algorithm enables fast authentication within only 3 seconds, meanwhile achieves reasonable performance in recognition. We test the proposed method in a controlled laboratory experiment with 50 subjects. Finger ECG signals are collected using a mobile device at different times and physical statues. At the first stage, a ``General'' NN model is constructed based on data from the cohort and used for preliminary screening, while at the second stage ``Personal'' NN models constructed from single individual's data are applied as fine-grained identification. The algorithm is tested on the whole data set, and on different sizes of subsets (5, 10, 20, 30, and 40). Results proved that the proposed method is feasible and reliable for individual authentication, having obtained average False Acceptance Rate (FAR) and False Rejection Rate (FRR) below 10% for the whole data set.

The chaotic system and cryptography have some common features. Due to the close relationship between chaotic system and cryptosystem, researchers try to combine the chaotic system with cryptosystem. In this study, security analysis of an encryption algorithm which aims to encrypt the data with ECG signals and chaotic functions was performed using the Logistic map in text encryption and Henon map in image encryption. In the proposed algorithm, text and image data can be encrypted at the same time. In addition, ECG signals are used to determine the initial conditions and control parameters of the chaotic functions used in the algorithm to personalize of the encryption algorithm. In this cryptanalysis study, the inadequacy of the mentioned process and the weaknesses of the proposed method have been determined. Encryption algorithm has not sufficient capacity to provide necessary security level of key space and secret key can be obtained with only one plaintext/ciphertext pair with chosen-plaintext attack.

As a plethora of wearable devices are being introduced, significant concerns exist on the privacy and security of personal data stored on these devices. Expanding on recent works of using electrocardiogram (ECG) as a modality for biometric authentication, in this work, we investigate the possibility of using personal ECG signals as the individually unique source for physical unclonable function (PUF), which eventually can be used as the key for encryption and decryption engines. We present new signal processing and machine learning algorithms that learn and extract maximally different ECG features for different individuals and minimally different ECG features for the same individual over time. Experimental results with a large 741-subject in-house ECG database show that the distributions of the intra-subject (same person) Hamming distance of extracted ECG features and the inter-subject Hamming distance have minimal overlap. 256-b random numbers generated from the ECG features of 648 (out of 741) subjects pass the NIST randomness tests.

The design of low power chip for IoT applications is very challenge, especially for self-powered wireless sensors. Achieving ultra low power requires both system level optimization and circuit level innovation. This paper presents a continuous-in-time and discrete-in-amplitude (CTDA) system architecture that facilitates adaptive data rate sampling and clockless implementation for a wireless sensor SoC.

Sophisticated technologies realized from applying the idea of biometric identification are increasingly applied in the entrance security management system, private document protection, and security access control. Common biometric identification involves voice, attitude, keystroke, signature, iris, face, palm or finger prints, etc. Still, there are novel identification technologies based on the individual's biometric features under development [1-4].

This paper presents a novel electrocardiogram (ECG) compression method for e-health applications by adapting an adaptive Fourier decomposition (AFD) algorithm hybridized with a symbol substitution (SS) technique. The compression consists of two stages: first stage AFD executes efficient lossy compression with high fidelity; second stage SS performs lossless compression enhancement and built-in data encryption, which is pivotal for e-health. Validated with 48 ECG records from MIT-BIH arrhythmia benchmark database, the proposed method achieves averaged compression ratio (CR) of 17.6-44.5 and percentage root mean square difference (PRD) of 0.8-2.0% with a highly linear and robust PRD-CR relationship, pushing forward the compression performance to an unexploited region. As such, this paper provides an attractive candidate of ECG compression method for pervasive e-health applications.