It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient’s criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.
Background
The significant features like an amplitude and intervals of electrocardiograph or P-QRS-T wave represent the functionality of the heart. Accurate extraction of these features helps in capturing characteristics of the signal helpful for the detection of cardiac abnormalities. In this paper, a novel signal folding-based algorithm is proposed to obtain detailed information about the complex morphology of signal. It explores the denoising and feature extraction of the specific ECG signals.
Results
The experimental study conducted using MIT-BIH Arrhythmia database ECG records with known conditions of left bundle branch block, right bundle branch block, Wolff-Parkinson-White syndrome beats has been considered. Heart rate values for selected ECG records from MIT-BIH dataset and synthetic signals from ECG simulator yielded the same values and thus validate our approach.
Conclusion
The proposed algorithm determines the heart rate, percentage leakage around the peak and is capable of folding a signal very efficiently based on detected R peaks and period-dependent gate(window).
In ambulatory ECG monitoring application energy efficient signal acquisition plays significant role in ensuring the lifetime of resource constrained WBAN node . Most of the Compressive Sensing (CS) algorithms employ fixed mother wavelet choice for decomposition phase, resulting in incorrect block-wise data representation thus yielding higher PRD, lower CR and subsequent faster energy consumption rate. To overcome this design issue a novel minimum PRD based adaptive best mother wavelet (ABMW) selection algorithm has been proposed individually for each block and tested for compression of ECG signals in emergent CS paradigm over three datasets. Performance metrics illustrate that the proposed algorithm supports true representation of the physiological events, is energy efficient and faster than its predecessors and has an average execution delay of 1.7 seconds for compression and recovery of 10 seconds ECG data. Simulation results show that proposed algorithm achieved average PRD of 1.141733, CR of 63.77417 and SNR of 40.63878. The proposed algorithm achieved average PRD of 3.59 , execution speed of 2.09 seconds ,CR of 62.32, SNR of 29.5dB and energy consumption is around 1.64E-04 which is very near to average energy consumption values for both MIT-BIH ,PTB datasets and 24-bit acquired ECG data
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