Comprehensive Medication Management (CMM): Application of a new pharmaceutical practice in Onco-Hematology.

The integration of a large number of drugs, such as antineoplastic agents and cancer-related supportive care drugs, into the management of cancer patients exposes them to an increased number of drug-related problems (DRP). Clinical pharmacists contribute to drug management by actively intervening in detected DRP. The aim of this study is to assess the impact of the applying a clinical pharmacist-driven Comprehensive Medication Management (CMM) service to Onco-hematology patients. This prospective interventional study was carried out over six-month duration, specifically from November 06, 2022 to April 5, 2023 in the oncology and hematology departments of the EHU Oran. The adherence to treatment was evaluated using the 8-item Morisky Medication Adherence Scale (MMAS). Whereas Data related to the patient's general condition and medication history was assessed using the Pharmaceutical Care Network Europe (PCNE) Classification for Drug-Related Problems V9.1. Among the 130 patients included in the study, a total of 879 DRP were identified, with a mean of 6.78 (±1.72) DRP/patient, half of which were related to efficacy (51%). Almost half of our sample (44.6%) did not adhere to their treatment. The most frequent cause of DRP, accounting for (19.9%) of the cases, was the inappropriate administration by a health professional. A total of 875 pharmaceuticals interventions (PI) were proposed, 67.2% of which were focused at the drug level. The PI acceptance rate was 94.1%. The integration of CMM services in Onco-Hematology played an important role in optimizing dosing regimen and treatment administration methods, as well as preventing iatropathology in the management of cancer patients.

Epidemiology of breast cancer in women based on diagnosis data from oncologists and senologists in Algeria.

BACKGROUND: Breast cancer (BC) is a major health issue threatening women's life. No reliable epidemiological data on BC diagnosed by oncologists/senologists are available in Algeria. METHODS: The BreCaReAl study, a non-interventional prospective cohort study, included adult women with confirmed BC in Algeria. Disease incidence, patients and disease characteristics, treatment patterns, and mortality rate were recorded up to 12 months of follow-up. RESULTS: Overall, 1,437 patients were analysed: median age was 48 [41;57] years and 337 (23.5%) women had a family history of BC. BC incidence was 22.3 (95% CI: 21.5; 23.2) cases per 100,000 inhabitants over 8 months. Delayed diagnosis was reported in 400 (29.2%) patients. First line of treatments were mainly chemotherapy and surgery. Twenty-eight serious adverse events were reported including 10 (37.0%) events which led to death. Mortality rate reached 3.2% at 12 months CONCLUSION: A delayed diagnosis highlights the importance of implementing more effective screening strategies.

From Korotkoff and Marey to automatic non-invasive oscillometric blood pressure measurement: does easiness come with reliability?

The auscultatory technique remains the point of reference for the validation of non-invasive blood pressure measurement devices, although the exact origin of the Korotkoff sounds is still debated and comparison with intra-arterial measurement shows limits and pitfalls. Automatic oscillometric devices are now widely used by nurses, physicians, and patients. However, many available devices have not been duly validated. Moreover, they calculate systolic and diastolic blood pressures using undisclosed algorithms. Therefore, these devices are not interchangeable, and their reliability may be questionable in some clinical situations. Nevertheless, oscillometry is increasingly used, beside NIBP, for the assessment of central blood pressure and systemic arterial wall stiffness. Awareness of its limits and causes of error is all the more necessary.

The quantification of the QT-RR interaction in ECG signal using the detrended fluctuationanalysis and ARARX modelling.

In this paper, the detrended fluctuation analysis DFA is used to investigate and quantify the QT-RR interaction in different pathologic cases in order to distinguish between them. The study is carried out on the ECG signals of MIT-BIH universal database. Different ECG signals related to cardiac pathological cases are concerned with this study. These are: Premature Ventricular Contraction (PVC) (9 cases), Right Bundle Branch Block (RBBB) (4 cases), Left Bundle Branch Block (LBBB) (2 cases), Atrial Premature Beat (APB) (4 cases), Paced Beat (PB) (4 cases), and other pathologic cases with different severity (10 cases). All this cases are compared to the 15 normal cases. The obtained results show that the DFA can identify the healthy subject from the pathologic cases according to the values of the scaling exponent α. The results indicate that α varies between 0.5 and 1 in all cases which means that there is a long range correlation in RR and QT series. The QT and RR series are also modelled using the ARARX model. The parameters of the model are then extracted. The power spectral density (PSD) is estimated by using these parameters in order to provide further information about the causal interactions within the signals and also to determine the power scaling exponent ß. This scaling exponent confirms the relationship between RR and QT intervals in all the studied cases except in APB and PB cases where the behaviour is similar to that of the white noise. The QT variability degrees are calculated and the DFA is applied on it. The obtained results show a long range correlation between RR and QT intervals in all cases and an ambiguity in the APB case. The DFA is compared to the Poincaré method in order to evaluate the algorithm performance using the Fuzzy Sugeno classifier is used for this purpose.

Analysis of the QT-RR variability interactions using the NARMAX model.

In this paper a new approach is used in order to evaluate and quantify the interactions between the QT and RR intervals. This is achieved after the identification of the RR and QT series with a hybrid model (the non-linear autoregressive moving average with exogenous input (NARMAX)). This identification follows two steps: the first is a linear parametric identification corresponding to the MA model, whereas the second is a non-linear identification using the NARX model. The power spectral density PSD of RR and QT is computed by using the monovariate part of this model (MA model). The QT-related RR series is obtained by using the bivariate part corresponding to the NARX model and its PSD is determined by using the autoregressive method. Then a cross-spectral and the coherence function were determined in order to confirm the obtained results. Different heart pathology cases were selected to evaluate the approach: the normal case, the cases which represent long QT intervals and some other cases which represent short QT intervals. They were taken from the MIT BIH database. The results show that every case illustrates two frequencies; the first in the low frequency band LF and the second in the high frequency band HF. In the normal case and long QT interval cases, the LF was predominating in the QT, RR and in QT-related RR power spectral density PSD. In the short QT interval cases the HF was much larger in all cases. The obtained results were compared to the poincaré plot method which confirms it; however, the NARMAX model can distinguish between normal and pathologic cases with a great precision (p < 0.001). In addition, the QT variability index QTVI is computed and represented by a box plot which expresses the relationship between QT and RR intervals. The QTVI shows a large variability in the short QT interval cases, whereas it shows a small and a negative variability in the long QT interval case.

Emotion recognition from physiological signals.

Emotion recognition is one of the great challenges in human-human and human-computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.

Noise and baseline wandering suppression of ECG signals by morphological filter.

Electrocardiogram (ECG) signals describe the electrical activity of the heart, and are universally by physicists in the diagnosis of cardiac pathologies. However, during the acquisition of ECGs they are often contaminated with different sources of noise, making interpretation difficult. Different techniques have been used to filter the ECG signal, in order to optimize the signal to noise ratio (S/N). In this paper, an approach based on morphological filtering is developed in order to filter the ECG. Morphological filtering is concerned with the detection of the ECG morphology, therefore allowing the suppression of noises and particularly baseline wandering. The implemented filter is evaluated using signals taken from the MIT-BIH ECG universal database. The results show that the performance of this filter is good compared with other filtering techniques.

The effectiveness of the wavelet transforms method in the heart sounds analysis.

Heart sounds can be used more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and non-stationary that they are very difficult to analyse in time or frequency domains. We have studied the extraction of features from heart sounds in the time-frequency domain for recognition of heart sounds through time-frequency analysis. The application of wavelet transform for the heart sounds is thus described. The performance of continuous wavelet transform, discrete wavelet transform and packet wavelet transform is discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify clinical usefulness of our extraction methods for recognition of heart sounds.

ECG signal processing using multiresolution analysis.

In this paper, multiresolution analysis using wavelets is discussed and evaluated in ECG signal processing. The approach we developed for processing the ECG signals uses two steps. In the first step, we implement an algorithm based on multiresolution analysis using discrete wavelet transform for denoising the ECG signals. The results we obtained on MIT-BIH ECG signals show good performance in denoising ECG signals. In the second step, multiresolution analysis is applied for QRS complex detection. It is shown that with such analysis, the QRS complex can be distinguished from high P or T waves, baseline drift and artefacts. The results we obtained on ECG signals from the MIT-BIH database show a detection rate of QRS complexes above 99.8% (sensitivity=99.88% and predictivity=99.89%), and a total detection failure of 0.24%.

Filtering and classification of phonocardiogram signals using wavelet transform.

Auscultation is a technique in which a stethoscope is used to listen to the sounds of the heart. Structural defects of the heart are often reflected in the sounds the heart produces, and auscultation provides clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as clinical tool, it is difficult to analyse heart sound signals in the time or frequency domain. Thus phonocardiogram (PCG), recording of heart sounds has many advantages over traditional auscultation, in that they may be replayed and analysed for time and frequency information. Using discrete wavelet transform, the signal is decomposed and reconstructed without significant loss of information in the signal content. The error of rebuilding can be considered as an important parameter in the classification of the pathological severity of the phonocardiogram signals. Variation of this parameter is very sensitive to the murmur importance in PCG signals.

Computerized heart sounds analysis.

This paper is concerned with a synthesis study of the fast Fourier transform (FFT), the short-time Fourier transform (STFT), the Wigner distribution (WD) and the wavelet transform (WT) in analysing the phonocardiogram signal (PCG). It is shown that these transforms provide enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency (TF) PCG signal characteristics and consequently aid diagnosis. Similarly, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties. The studied techniques (FT, STFT, WD, CWT, DWT and PWT) of analysis can thus be regarded as complementary in the TF analysis of the PCG signal; each will relate to a part distinct from the analysis in question.

Automatic measure of the split in the second cardiac sound by using the wavelet transform technique.

This paper is concerned with the identification and automatic measure of the split in the second heart sound (S2) of the phonocardiogram signal (PCGs) for normal or pathological case. The second heart sound S2 consists of two acoustic components A2 and P2, the former is due to the closure of the aortic valve and the latter is due to the closure of the pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as "split". A automatic technique based on the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT) is developed in this paper to measure the split of the second cardiac sound (S2) for the normal and pathological cases of the PCG signals. To quantify the splitting, the two components in S2 (i.e. A2 and P2) are identified and, the delay between the two components can be estimated. It is shown that the wavelet transform can provide best information and features of the split of S2 and the major components (A2 and P2) and consequently aid in medical diagnosis.

Analysis and study of the variation of splitting in the second heartbeat sound of wavelet transform.

The second heart sound, S2, consists of two acoustic components, A2 and P2. The former is due to the closure of the aortic valve and the latter is due to the closure of the pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as the 'split'. A technique based on discrete wavelet transform (DWT) and continuous wavelet transform (CWT) is developed in this paper to measure the split. To quantify splitting, two components in S2 (i.e. A2 and P2) are identified, and the delay between the two components can be estimated. One normal case and three pathological cases (mitral stenosis, pulmonary stenosis and atrial septal defect) are considered in this study. The split is measured for each S2 sound of the considered signals. The split normally varies in duration over the cardiac cycle. In certain pathologies such as ASD (atrial septal defect) or PS (pulmonary stenosis), the split becomes fixed over the cardiac cycle. The main part of this paper consists of the identification and measurement of the S2 split. The study confirms the notion of 'variable splitting' for normal phonocardiogram and 'fixed splitting' for ASD and PS cases. This paper relates also to the establishment of statistical parameters to make a distinction between normal and pathological cases of phonocardiogram signals.

Detection of the QRS complex by linear prediction.

The electrocardiogram (ECG) represents the electrical activity of the heart. It is characterized by its recurrent or periodic behaviour with each beat. Each recurrence is composed of a wave sequence consisting of P, QRS and T-waves, where the most characteristic wave set is the QRS complex. In this paper, we have developed an algorithm for detection of the QRS complex. The algorithm consists of several steps: signal-to-noise enhancement, linear prediction for ECG signal analysis, nonlinear transform, moving window integrator, centre-clipping transformation and QRS detection. Linear prediction determines the coefficients of a forward linear predictor by minimizing the prediction error by a least-square approach. The residual error signal obtained after processing by the linear prediction algorithm has very significant properties which will be used to localize and detect QRS complexes. The detection algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with the Pan and Tompkins QRS detection method. The results we obtain show that our method performs better than this method. Our algorithm results in fewer false positives and fewer false negatives.

Time-frequency analysis of the second cardiac sound in phonocardiogram signals.

The paper is concerned with the analysis of the phonocardiogram signals (PCG) in the time-frequency domain. Three techniques are studied and evaluated in PCG signal analysis. These are the short time Fourier transform (STFT), the Wigner distribution function (WD) and the continuous wavelet transforms (CWTs). The analysis is first carried out on the second cardiac sound (S2) in order to show the aptitude of each method in distinguishing the internal components of this sound. The results we obtain show that the STFT cannot detect the two internal components of S2 (A2 and P2, respectively, the aortic and pulmonary components). The WD can provide time-frequency characteristics of S2, but with insufficient diagnostic information: the two components are not accurately detected and appear to be only one component. It is found that the CWT (it can also provide the time-frequency characteristic of S2) is capable of detecting its two components, A2 and P2, allowing therefore the measurement of the delay between them. This delay, called the split, is very important in the diagnosis of many pathological cases, as it is emphasized in the results we obtain by applying the CWT on different pathological cases (mitral stenosis, pulmonary stenosis and atrial septal defect).

Analysis of the second heart sound using continuous wavelet transform.

This paper is concerned with a synthesis study of Continuous Wavelet Transform (CWT) in analysing the second heart sound of the phonocardiogram (PCG). The second heart sound S2 consists of two major components (A2 and P2) with a time delay between them which is very important for a diagnosis. It is shown that CWT provides enough features of these components of time, frequency and time delay to aid diagnosis.

A Powerful Algorithm to aid Cardiac Arrhythmia Diagnosis.

The Electrocardiogram (ECG), represents the electrical activity of the heart. It is characterised by a number of waves P, QRS, T which are correlated to the status of the heart activity. In this paper, the aim is to present a powerful algorithm to aid cardiac diagnosis. The approach used is based on a determinist method, that of the tree decision. However, the different waves of the ECG signal need to be identified and then measured following a signal to noise enhancement. Signal to noise enhancement is performed by a combiner linear adaptive filter whereas P, QRS, T wave identification and measurement are performed by a derivative approach. Results obtained on simulated and real ECG signals are shown to be highly, satisfactory in the aid of cardiac arrhythmia diagnosis, such as junctionnal escapes, blocks, etc.