Journal of Healthcare Engineering

Neonatal diseases are among the main causes of morbidity and a significant contributor to underfive mortality in the world. There is an increase in understanding of the pathophysiology of the diseases and the implementation of different strategies to minimize their burden. However, improvements in outcomes are not adequate. Limited success is due to different factors, including the similarity of symptoms, which can lead to misdiagnosis, and the inability to detect early for timely intervention. In resource-limited countries like Ethiopia, the challenge is more severe. Low access to diagnosis and treatment due to the inadequacy of neonatal health professionals is one of the shortcomings. Due to the shortage of medical facilities, many neonatal health professionals are forced to decide the type of disease only based on interviews. They may not have a complete picture of all variables that have a contributing effect on neonatal disease from the interview. This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia, necrotizing enter colitis (NEC), and respiratory distress syndrome. These diseases account for 75% of neonatal deaths. The dataset has been obtained from the Asella Comprehensive Hospital. It has been collected between 2018 and 2021. The developed stacking model was compared to three related machine-learning models XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). The proposed stacking model outperformed the other models, with an accuracy of 97.04%. We believe that this will contribute to the early detection and accurate diagnosis of neonatal diseases, especially for resource-limited health facilities.

Heart rate (HR) estimation from multisensor PPG signals suffers from the dilemma of inconsistent computation results, due to the prevalence of bio-artifacts (BAs). Furthermore, advancements in edge computing have shown promising results from capturing and processing diversified types of sensing signals using the devices of Internet of Medical Things (IoMT). In this paper, an edge-enabled method is proposed to estimate HRs accurately and with low latency from multisensor PPG signals captured by bilateral IoMT devices. First, we design a real-world edge network with several resource-constrained devices, divided into collection edge nodes and computing edge nodes. Second, a self-iteration RR interval calculation method, at the collection edge nodes, is proposed leveraging the inherent frequency spectrum feature of PPG signals and preliminarily eliminating the influence of BAs on HR estimation. Meanwhile, this part also reduces the volume of sent data from IoMT devices to compute edge nodes. Afterward, at the computing edge nodes, a heart rate pool with an unsupervised abnormal detection method is proposed to estimate the average HR. Experimental results show that the proposed method outperforms traditional approaches which rely on a single PPG signal, attaining better results in terms of the consistency and accuracy for HR estimation. Furthermore, at the designed edge network, our proposed method processes a 30 s PPG signal to obtain an HR, consuming only 4.24 s of computation time. Hence, the proposed method is of significant value for the low-latency applications in the field of IoMT healthcare and fitness management.

Deep neural networks (DNNs) have been widely adopted in many fields, and they greatly promote the Internet of Health Things (IoHT) systems by mining health-related information. However, recent studies have shown the serious threat to DNN-based systems posed by adversarial attacks, which has raised widespread concerns. Attackers maliciously craft adversarial examples (AEs) and blend them into the normal examples (NEs) to fool the DNN models, which seriously affects the analysis results of the IoHT systems. Text data is a common form in such systems, such as the patients’ medical records and prescriptions, and we study the security concerns of the DNNs for textural analysis. As identifying and correcting AEs in discrete textual representations is extremely challenging, the available detection techniques are still limited in performance and generalizability, especially in IoHT systems. In this paper, we propose an efficient and structure-free adversarial detection method, which detects AEs even in attack-unknown and model-agnostic circumstances. We reveal that sensitivity inconsistency prevails between AEs and NEs, leading them to react differently when important words in the text are perturbed. This discovery motivates us to design an adversarial detector based on adversarial features, which are extracted based on sensitivity inconsistency. Since the proposed detector is structure-free, it can be directly deployed in off-the-shelf applications without modifying the target models. Compared to the state-of-the-art detection methods, our proposed method improves adversarial detection performance, with an adversarial recall of up to 99.7% and an F1-score of up to 97.8%. In addition, extensive experiments have shown that our method achieves superior generalizability as it can be generalized across different attackers, models, and tasks.

In Internet of Health Things (IoHT) systems, there is a two-hop network structure between the authentication server TA, Internet of Things Connector (IotC), and wearable sensor (WS). Attackers can use the sensor layer network (the first hop) between the IotC and WS to steal patient’s health-related information and undermine the security of the system and the privacy of sensitive information. To address this threat, this study proposes a lightweight identity authentication and key agreement protocol for third-party authentication servers TA, IotC, and WS. The results of the formal security proof, BAN logic analysis, and AVISPA tool simulation show that the scheme proposed in this study has an ideal security performance and can meet the security requirements of IoHT. In terms of performance, the proposed scheme could dynamically construct a sensor layer network (the first hop) and offline networking according to the diagnostic needs of doctors. Compared with other related protocols, the proposed scheme can significantly reduce the computing resource requirements of IotC and server TA and the resource requirements of database I/O operation of server TA in the application scenario of concurrent access of multiple WS nodes.

The treadmill is widely used in running fatigue experiments, and the variation of plantar mechanical parameters caused by fatigue and gender, as well as the prediction of fatigue curves by a machine learning algorithm, play an important role in providing different training programs. This experiment aimed to compare changes in peak pressure (PP), peak force (PF), plantar impulse (PI), and gender differences of novice runners after they were fatigued by running. A support vector machine (SVM) was used to predict the fatigue curve according to the changes in PP, PF, and PI before and after fatigue. 15 healthy males and 15 healthy females completed two runs at a speed of 3.3 m/s ± 5% on a footscan pressure plate before and after fatigue. After fatigue, PP, PF, and PI decreased at hallux (T1) and second-fifth toes (T2–5), while heel medial (HM) and heel lateral (HL) increased. In addition, PP and PI also increased at the first metatarsal (M1). PP, PF, and PI at T1 and T2–5 were significantly higher in females than in males, and metatarsal 3–5 (M3–5) were significantly lower in females than in males. The SVM classification algorithm results showed the accuracy was above average level using the T1 PP/HL PF (train accuracy: 65%; test accuracy: 75%), T1 PF/HL PF (train accuracy: 67.5%; test accuracy: 65%), and HL PF/T1 PI (train accuracy: 67.5%; test accuracy: 70%). These values could provide information about running and gender-related injuries, such as metatarsal stress fractures and hallux valgus. Application of the SVM to the identification of plantar mechanical features before and after fatigue. The features of the plantar zones after fatigue can be identified and the learned algorithm of plantar zone combinations with above-average accuracy (T1 PP/HL PF, T1 PF/HL PF, and HL PF/T1 PI) can be used to predict running fatigue and supervise training. It provided an important idea for the detection of fatigue after running.

Patient safety and involvement of the patients in their safety engagement activities are considered the most important elements in the healthcare professions due to their impact on various individual and organizational outcomes. The study used responses of 456 patients. The simple random sampling (SRS) technique was used to collect data from the respondents. The researcher used individuals as the unit of analysis in this study. The results revealed that patient safety engagement had a positive significant effect on patient safety. When the mediating variable of self-efficacy was analyzed, it showed a significant mediated effect on patient safety. Therefore, it was concluded that self-efficacy mediated the relationship between patient safety engagement and patient safety. The findings of the current study convey that engagement of the patient in the practices for patient safety is predicted through the level of self-efficacy of the patient. The study discussed various implications for theory and practice. The study also discussed potential avenues for future research.