Mirjam E. Jonkman

R wave detection using Coiflets wavelets

Accurate detection of QRS complexes is important for ECG signal analysis. In this paper, a generic algorithm using Coiflet wavelets is introduced to improve the detection of QRS complexes in Arrhythmia ECG Signals that suffer from: 1) non-stationary effects, 2) low Signal-to-Noise Ratio, 3) negative QRS polarities, 4) low QRS amplitudes, and 5) ventricular ectopics. The algorithm achieves high detection rates by using a signal-to-noise ratio threshold instead of predetermined static thresholds. The performance of the algorithm was tested on 48 records of the MIT/BIH Arrhythmia Database. It was shown that this adaptive approach results in accurate detection of the QRS complex and that Coiflet1 achieves better detection rate than the other Coiflet wavelets.

Heart rate variability and the acceleration plethysmogram signals measured at rest

It is well-known that the electrocardiogram (ECG) is a non-invasive method that can be used to measure heart rate variability (HRV). Photoplethysmogram (PPG) signals also reflect the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Photoplethysmography is a non-invasive, safe, and easy-to-use technique that has been developed for experimental use in vascular disease. A useful algorithm for a-wave detection in the acceleration plethysmogram (APG, the second derivative of the PPG) is introduced to determine the interval between successive heartbeats and heart rate variability. In this study, finger-tip PPG signals were recorded for twenty seconds from 27 healthy subjects measured at rest. The use of the aa interval in APG signals showed very promising results in calculating the HRV statistical indices, SDNN and rMSSD.

Applying the APG to measure Heart Rate Variability

The Acceleration Plethysmogram (APG) is used to calculate the heart rate (HR) and HRV. The APG is an optical technique that has been developed for experimental use in vascular diseases. It is considered a promising tool that may replace some of the current traditional cardiovascular diagnostic tools. The performance of the proposed algorithm when tested on 26 records measured at rest showed very promising results. Our results demonstrate that the HRV indices, SDNN and rMSSD can be calculated using APG signals collected at rest.

Premature atrial complexes detection using the Fisher Linear Discriminant

Currently, no reliable method exists to detect premature atrial complexes (PAC). The detection of PACs is clinically essential to predict supraventricular tachycardia, postoperative atrial fibrillation and paroxysmal atrial fibrillation. We propose an algorithm for intra-class classification that includes an analysis of the R-R time series. In the pre-processing phase, we used Butter worth filters to remove the baseline wander and the other noise. In the feature extraction phase, we detected the RR interval duration and the distance between the occurrence of P wave and T wave. Using these features we applied Fisherpsilas Linear Discriminant to create a criterion that can be used for classification. Combining pre-processing, feature extraction and Fisherpsilas Linear Discriminant we succeed in separating Normal and PAC beats with 99% Accuracy.

The Application of Wavelet and Feature Vectors to ECG Signals

TheElectrocardiogram (ECG) is one of the most commonly known biological signals. Traditionally ECG recordings are analysed in the time-domain by skilled physicians. However, pathological conditions may not always be obvious in the original time-domain signal. Fourier analysis provides frequency information but has the disadvantage that time characteristics will be lost. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. Here a new method, the combination of wavelet analysis and feature vectors, is applied with the intent to investigate its suitability as a diagnostic tool. ECG signals with normal and abnormal beats were examined. There were two stages in analysing ECG signals: feature extraction and feature classification. To extract features from ECG signals, wavelet decomposition was first applied and feature vectors of normalised energy and entropy were constructed. These feature vectors were used to classify signals. The results showed that normal beats and abnormal beats composed different clusters in most cases. In conclusion, the combination of wavelet transform and feature vectors has shown potential in detecting abnormalities in an ECG recording. It was also found that normalised energy and entropy are features, which are suitable for classification of ECG signals.

Recognition of T waves in ECG signals

The method described in this paper deals with the problems of T-wave detection in ECG signals. Determining the position of a T-wave can be complicated, due to the ambiguous and changing form of the complex and the presence of noise. We developed a method to detect T-waves in noisy signals. The performance of the proposed method was tested on 33 records of the MIT/BIH Arrhythmia Database resulting in 0.48% incorrectly detected T waves.

P wave demarcation in electrocardiogram

Efficient and effective feature extraction algorithms are required in the analysis of long records electrocardiographic (ECG) signals. In this paper a computationally efficient method is proposed as a feature extractor for P waves in ECG signals. The performance of the proposed algorithm was tested on 29 records of the MIT/BIH Arrhythmia Database resulting in 0.72% incorrectly detected P waves.

An acquisition method for the MLR of auditory evoked potentials

The study is focused on the recording of the auditory evoked potential to stimuli that result in binaural (two-ear) interaction. The auditory evoked potential is derived from small bioelectric potentials recorded from the scalp. The AEP is categorized on the basis of the latency of the response following the auditory stimulus. For example, the auditory brainstem response (ABR) occurs in the first 20 ms after the stimulus, the middle latency response (MLR) from 20 to 70 ms, and the slow vertex response (SVR) up to 500 ms after stimulation. The study of auditory evoked potentials may provide insight in the mechanism of auditory processing in the brain. The study presents a methodology to measure AEP related to binaural hearing.

A Review of Ransomware Families and Detection Methods

Ransomware has become a significant problem and its impact is getting worse. It has now become a lucrative business as it is being offered as a service. Unlike other security issues, the effect of ransomware is irreversible and difficult to stop. This research has analysed existing ransomware classifications and its detection and prevention methods. Due to the difficulty in categorizing the steps none of the existing methods can stop ransomware. Ransomware families are identified and classified from the year 1989 to 2017 and surprisingly there are not much difference in the pattern. This paper concludes with a brief discussion about the findings and future work of this research.

A Novel Approach for Steganography App in Android OS

The process of hiding information in a scientific and artistic way is known as Steganography. The information hidden cannot be easily retrieved or accessed and is unidentifiable. In this research, some of the existing methods for image steganography has been explained. These are LSB (Least Significant Bits) substitution method, DCT (Discrete Cosine Transform) and DWT (Discrete Wavelet Transform). A comparative analysis of these techniques depicted that LSB is the easiest and most efficient way of hiding information. But this technique can be easily attacked and targeted by attackers as it changes the image resolution. Using LSB technique an application was created for image steganography because it hides the secret message in binary coding. To overcome this problem a RSA algorithm was used in the least significant bits of pixels of image. Additionally, a QR code was generated in the encryption process to make it more secure and allow the quality of the image to remain as intact, as it was before the encryption. PNG and JPEG formats were used as the cover image in the app and findings also indicated the data was fully recovered.