An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications.

Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone's life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao's, and Xiao's methods).

Erratum: Sandeep P., et al. An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks. Sensors 2015, 15, 15067-15089.

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Erratum: Sandeep P., et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network. Sensors 2017, 17, 69.

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A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network.

Wireless sensor networks (WSNs) provide noteworthy benefits over traditional approaches for several applications, including smart homes, healthcare, environmental monitoring, and homeland security. WSNs are integrated with the Internet Protocol (IP) to develop the Internet of Things (IoT) for connecting everyday life objects to the internet. Hence, major challenges of WSNs include: (i) how to efficiently utilize small size and low-power nodes to implement security during data transmission among several sensor nodes; (ii) how to resolve security issues associated with the harsh and complex environmental conditions during data transmission over a long coverage range. In this study, a secure IoT-based smart home automation system was developed. To facilitate energy-efficient data encryption, a method namely Triangle Based Security Algorithm (TBSA) based on efficient key generation mechanism was proposed. The proposed TBSA in integration of the low power Wi-Fi were included in WSNs with the Internet to develop a novel IoT-based smart home which could provide secure data transmission among several associated sensor nodes in the network over a long converge range. The developed IoT based system has outstanding performance by fulfilling all the necessary security requirements. The experimental results showed that the proposed TBSA algorithm consumed less energy in comparison with some existing methods.

Relationship of short-term blood pressure variability with carotid intima-media thickness in hypertensive patients.

BACKGROUND: High blood pressure (BP) is among significant risk factor for stroke and other vascular occurrences, it experiences nonstop fluctuations over time as a result of a complex interface among cardiovascular control mechanisms. Large blood pressure variability (BPV) has been proved to be promising in providing potential regulatory mechanisms of the cardiovascular system. Although the previous studies also showed that BPV is associated with increased carotid intima-media thickness (IMT) and plaque, whether the correlation between variability in blood pressure and left common carotid artery-intima-media thickness (LCCA-IMT) is stronger than right common carotid artery-intima-media thickness (RCCA-IMT) remains uncertain in hypertension. METHODS: We conduct a study (78 hypertensive subjects, aged 28-79) to evaluate the relationship between BPV and carotid intima-media thickness in Shenzhen. The blood pressure was collected using the 24 h ambulatory blood pressure monitoring, and its variability was evaluated using standard deviation (SD), coefficient of variation (CV), and average real variability (ARV) during 24 h, daytime and nighttime. All the IMT measurements are collected by ultrasound. RESULTS: As the results showed, 24 h systolic blood pressure variability (SBPV) evaluated by SD and ARV were significantly related to LCCA-IMT (r(1) = 0.261, P = 0.021; r(1) = 0.262, P = 0.021, resp.). For the daytime diastolic blood pressure variability (DBPV), ARV indices were significantly related to LCCA-IMT (r(1) = 0.239, P = 0.035), which differed form BPV evaluated by SD and CV. For the night time, there is no significant correlation between the BPV and IMT. Moreover, for all the subjects, there is no significant correlation between the BPV and RCCA-IMT/number of plaques, whereas, the SD, CV, and ARV of daytime SBP showed a positive correlation with LCCA-IMT (r(1) = 0.312, P = 0.005; r(1) = 0.255, P = 0.024; r(1) = 0.284, P = 0.012, resp.). Moreover, the ARV of daytime SBPV, 24 h SBPV and nighttime DBPV showed a positive correlation with the number of plaques of LCCA (r(1) = 0.356, P = 0.008; r(1) = 0.297, P = 0.027; r(1) = 0.278, P = 0.040, resp.). In addition, the number of plaques in LCCA had higher correlation with pulse pressure and diastolic blood pressure than that in RCCA. And multiple regression analysis indicated LCCA-IMT might not only be influenced by age or smoking but also by the SD index of daytime SBPV (p = 0.035). CONCLUSIONS: The results show that SBPV during daytime and 24 h had significant correlation with IMT, for the hypertensive subjects from the southern area of China. Moreover, we also found the daytime SBPV to be the best predictor for the progression of IMT in multivariate regression analysis. In addition, the present study suggests that the correlation between BPV and left common carotid artery-intima-media thickness/number of plaques is stronger than right common carotid artery-intima-media thickness/number of plaques.

Quantification of three-dimensional computed tomography angiography for evaluating coronary luminal stenosis using digital subtraction angiography as the standard of reference.

OBJECTIVE: We sought to evaluate the accuracy of quantitative three-dimensional (3D) CT angiography (CTA) for the assessment of coronary luminal stenosis using digital subtraction angiography (DSA) as the standard of reference. METHOD: Twenty-three patients with 54 lesions were referred for CTA followed by DSA. The CTA scans were performed with 256-slice spiral CT. 3D CTA were reconstructed from two-dimensional CTA imaging sequences in order to extract the following quantitative indices: minimal lumen diameter, percent diameter stenosis (%DS), minimal lumen area, and percent area stenosis (%AS). Correlation and limits of agreement were calculated using Pearson correlation and Bland-Altman analysis, respectively. The diagnostic performance and the diagnostic concordance of 3D CTA-derived anatomic parameters (%DS, %AS) for the detection of severe coronary arterial stenosis (as assessed by DSA) were presented as sensitivity, specificity, diagnostic accuracy, and Kappa statistics. Of which vessels with %DS >50% or with %AS >75% were identified as severe coronary arterial lesions. RESULT: The correlations of the anatomic parameters between 3D CTA and DSA were significant (r = 0.51-0.74, P < 0.001). Bland-Altman analysis confirmed that the mean differences were small (from -1.11 to 27.39%), whereas the limits of agreement were relatively wide (from ±28.07 to ±138.64%). Otherwise, the diagnostic accuracy (74.1% with 58.3% sensitivity and 86.7% specificity for DS%; 74.1% with 45.8% sensitivity and 96.7% specificity for %AS) and the diagnostic concordance (k = 0.46 for DS%; 0.45 for %AS) of 3D CTA-derived anatomic parameters for the detection of severe stenosis were moderate. CONCLUSION: 3D advanced imaging reconstruction technique is a helpful tool to promote the use of CTA as an alternative to assess luminal stenosis in clinical practice.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

Cost-efficient service selection and execution and blockchain-enabled serverless network for internet of medical things.

These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.

Quantitative Assessment for Self-Tracking of Acute Stress Based on Triangulation Principle in a Wearable Sensor System.

Due to the variation in factors surrounding humans, the physiological impact of stress is reported to be different for each individual. Thus, an efficient stress monitoring system needs to assess both the physiological and psychological impact of stress on individual basis and translate these assessments into an accurate quantitative metric that is of value to the individual. Therefore, this study proposed a logistic regression based model that integrates data from psychological Stress Response Inventory, biochemical (salivary cortisol), and physiological (HRV measures) domains via a principle of triangulation for achieving high reliability and consistency during stress assessment. With the proposed model, a mental stress index (MSI) based on the correlation between salivary cortisol and HRV time-/frequency-domain features were established. A total of 30 college students were recruited to verify the feasibility of proposed method by identifying targeted stressful event. The obtained results reveal that MSI values were sensitive to acute stress, and could predict the association level of normal individual to a stress group with approximately 97% accuracy. Findings from this study could provide potential insight on self-tracking and training of individual's stress with adoption of wearable sensor system in a dynamic setting.

Spatio-Temporal Based Descriptor for Limb Movement-Intent Characterization in EMG-Pattern Recognition System.

Electromyogram (EMG) pattern-recognition (PR) is the most widely adopted prostheses/rehabilitation robots control method that seamlessly support multi-degrees of freedom (MDF) function in an intuitive fashion. The feature extraction framework applied in such PR-based control essentially determines the control performance of the prosthetic device. Based on the drawbacks of the commonly utilized feature extraction methods, this study proposed a spatio-temporal-based feature set (STFS) that might optimally characterize EMG signal patterns even in the presence of white Gaussian noise (WGN) to realize consistently accurate and stable decoding of multiple classes of limb-movements. For benchmark evaluation, the performance of the proposed STFS method was examined in comparison to notable existing popular methods using high density surface EMG recordings from 8 amputees, with metrics such as classification error (CE) and feature-space separability index. Compared to the existing methods, the STFS recorded substantial reduction of up 16.73% even in the presence the inevitable WGN at p<; 0.05. Also, with principal component analysis concept, the proposed STFS feature-space indicates obvious class separability compared to the previous methods. Therefore, the newly proposed STFS method could potentially facilitate the realization of consistently accurate and reliable PR-based control for MDF prostheses/rehabilitation robots.

Heartbeats Based Biometric Random Binary Sequences Generation to Secure Wireless Body Sensor Networks.

Heartbeats based random binary sequences (RBSs) are the backbone for several security aspects in wireless body sensor networks (WBSNs). However, current heartbeats based methods require a lot of processing time (∼25-30 s) to generate 128-bit RBSs in real-time healthcare applications. In order to improve time efficiency, a biometric RBSs generation technique using interpulse intervals (IPIs) of heartbeats is developed in this study. The proposed technique incorporates a finite monotonic increasing sequences generation mechanism of IPIs and a cyclic block encoding procedure that extracts a high number of entropic bits from each IPI. To validate the proposed technique, 89 ECG recordings including 25 healthy individuals in a laboratory environment, 20 from MIT-BIH Arrhythmia Database, and 44 cardiac patients from the clinical environment are considered. By applying the proposed technique on the ECG signals, at most 16 random bits can be extracted from each heartbeat to generate 128-bit RBSs via concatenation of eight consecutive IPIs. And the randomness and distinctiveness of generated 128-bit RBSs are measured based on the National Institute of Standards and Technology statistical tests and hamming distance, respectively. From the experimental results, the generated 128-bit RBSs from both healthy subjects and patients can potentially be used as keys for encryption or entity identifiers to secure WBSNs. Moreover, the proposed approach is examined to be up to four times faster than the existing heartbeat-based RBSs generation schemes. Therefore, the developed technique necessitates less processing time (0-8 s) in real-time health monitoring scenarios to construct 128-bit RBSs in comparisons with current methods.

Heart-Carotid Pulse Wave Velocity a Useful Index of Atherosclerosis in Chinese Hypertensive Patients.

This study was designed to investigate the relationship between heart-carotid pulse wave velocity (hcPWV) and carotid intima-media thickness (CIMT) in hypertensive patients, and also to examine the effect of pre-ejection period (PEP) on it. Doppler ultrasound device was used to measure CIMT in left common carotid artery. Hypertensive patients were divided into normal (n = 36, CIMT ≤0.8 mm) and thickened (n = 31, CIMT > 0.8 mm) group. Electrocardiogram R-wave-based carotid pulse wave velocity (rcPWV) and aortic valve-carotid pulse wave velocity (acPWV) were calculated as the ratio of the travel length to the pulse transit time with or without PEP, respectively. CIMT has significant relations with rcPWV (r = 0.611, P < 0.0001) and acPWV (r = 0.384, P = 0.033) in thickened group. Moreover, CIMT showed stronger correlation with rcPWV than with acPWV in thickened group. Furthermore, both acPWV and rcPWV were determinant factors of CIMT in thickened group, independent of clinical confounders including age, gender, smoking behavior, systolic blood pressure, diastolic blood pressure, fasting blood glucose, total cholesterol, high-density lipoprotein cholesterol, antihypertensive medication, and plaque occurrence. However, similar results were not found in normal group. Since CIMT has been considered as an index of atherosclerosis, our results suggested that both rcPWV and acPWV could be useful indexes of atherosclerosis in thickened CIMT hypertensive patients. Additionally, if hcPWV is computed with heart-carotid pulse transit time, including PEP could improve the accuracy of atherosclerosis assessment in hypertensive patients.

A novel ultrasound based approach for lesion segmentation and its applications in gynecological laparoscopic surgery.

Laparoscopic ultrasound (LUS) has been widely utilized as a surgical aide in general, urological, and gynecological applications. Our study summarizes the clinical applications of laparoscopic ultrasonography in laparoscopic gynecologic surgery. Retrospective analyses were performed on 42 women subjects using laparoscopic surgery during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013. Specifically, the Esaote 7.5 × 10 MHz laparoscopic transducer was used to detect small residual lesions, as well as to assess, locate and guide in removing the lesions during laparoscopic operations. The findings of LUS were compared with those of preoperative trans-vaginal ultrasound, postoperative, and pathohistological examinations. In addition, a novel method for lesion segmentation was proposed in order to facilitate the laparoscopic gynecologic surgery. In our experiment, laparoscopic operation was performed using a higher frequency and more close to pelvic organs via laparoscopic access. LUS facilitates the ability of gynaecologists to find small residual lesions under laparoscopic visualization and their accurate diagnosis. LUS also helps to locate residual lesions precisely and provides guidance for the removal of residual tumor and eliminate its recurrence effectively. Our experiment provides a safer and more valuable assistance for clinical applications in laparoscopic gynecological surgery that are superior to trans-abdominal ultrasound and trans-vaginal ultrasound.