ICA-Derived Respiration Using an Adaptive R-peak Detector

Breathing Rate (BR) plays a key role in health deterioration monitoring. Despite that, it has been neglected due to inadequate nursing skills and insufficient equipment. ECG signal, which is always monitored in a hospital ward, is affected by respiration which makes it a highly appealing way for the BR estimation. In addition, the latter requires accurate R-peak detection, which is a continuing concern because current methods are still inaccurate and miss heart beats. This study proposes a frequency domain BR estimation method which uses a novel real-time R-peak detector based on Empirical Mode Decomposition (EMD) and a blind source ICA for separating the respiratory signal. The originality of the BR estimation method is that it takes place in the frequency domain as opposed to some of the current methods which rely on a time domain analysis, making the estimation more accurate. Moreover, our novel QRS detector uses an adaptive threshold over a sliding window and differentiates large Q-peaks from R-peaks, facilitating a more accurate BR estimation. The performance of our methods was tested on real data from Capnobase dataset. An average mean absolute error of less than 0.7 breath per minute was achieved using our frequency domain method, compared to 15 breaths per minute of the time domain analysis. Moreover, our modified QRS detector shows comparable results to other published methods, achieving a detection rate over 99.80%

Solving optimal control problem using max-min ant system

An improved ant colony algorithm for optimal control problems with box constrain on control functions is presented. The hypercube of the feasible controls as well as the time interval are initially discretized to approximate control problem into a discrete parameter selection problem. Then, the ant colony algorithm is applied to search for optimum parameters of approximated problem while a proper local search is also introduced to iteratively enhance the quality of solution. The results of numerical simulation on MATLAB environment illustrate the effectiveness of this method

Atrial fibrillation detection using photo-plethysmography and acceleration data at the wrist

Atrial fibrillation (AF) is a pathological cardiac condition leading to increased risk for embolic stroke. Screening for AF is challenging due to the paroxysmal and asymptomatic nature of the condition. We aimed to investigate whether an unobtrusive wrist-wearable device equipped with a photo-plethysmographic (PPG) and acceleration sensor could detect AF. Sixteen patients with suspected AF were monitored for 24 hours in an outpatient setting using a Holter ECG. Simultaneously, PPG and acceleration data were collected at the wrist. PPG data was processed to determine the timing of heartbeats and inter-beat-interval (IBI). Wrist acceleration and PPG morphology were used to discard IBIs in presence of motion artefacts. An ECG validated first-order Markov model was used to assess the probability of irregular rhythm due to AF using PPG-derived IBIs. The AF detection algorithm was compared with clinical adjudications of AF episodes after review of the ECG records. AF detection was achieved with 97 ± 2% sensitivity and 99 ± 3% specificity. Due to motion artefacts, the algorithm did not provide AF classification for an average of 36 ± 9% of the 24 hours monitoring. We concluded that a wrist-wearable device equipped with a PPG and acceleration sensor can accurately detect rhythm irregularities caused by AF in daily life