The impact of patients as trainers on registered nurses' patient engagement in primary care clinics: a qualitative study.

BACKGROUND: In Canada, primary care is usually the front door to health care for people with health issues. Among these primary care services are primary care clinics (PCC), where the competencies of registered nurses (RNs) are needed. However, nursing practice in PCCs is variable and sometimes suboptimal from one PCC to another. In 2019, the Quebec Ministry of Health and Social Services deployed a practical guide for RNs practicing in PCCs. This guide was intended to support best professional and interprofessional practices and enhance the quality of services offered according to a physical-social vision of care, interprofessional collaboration and partnership with the patient. The Formation de formateurs en première ligne (F2PL) project team developed a train-the-trainer educational intervention to support RNs in assimilating the content of this guide. This educational intervention is uncommon because it includes patients as trainers (PTs). PTs developed and provided andragogic content about patient's experience to enhance patient engagement. OBJECTIVE: To describe the impacts of the educational intervention provided by the PTs in nurses' patient engagement practices in PCCs. METHODS: A descriptive qualitative approach was used to describe in-depth changes in RNs' practices. Individual interviews were conducted with 10 RNs and 3 PTs to explore the changes in RNs' practice and the barriers and facilitators to adopting this new practice. An inductive and deductive thematic analysis was carried out according to a conceptual model of patient engagement (the Montreal Model), and emerging themes were condensed into propositions. To ensure credibility, a peer review was conducted with the F2PL team, which includes a patient co-leader. RESULTS: The educational intervention provided by PTs has impacted RNs' practice in 3 ways: awareness or reminding of general principles, updating commitment to already known principles and enhancing the development of new professional skills. CONCLUSIONS: PTs could effectively support the RNs' motivation to use patient engagement practices in primary care.

Empirical mode decomposition based ECG features in classifying and tracking ventricular arrhythmias.

Ventricular arrhythmias (VA) are life-threatening pathophysiological conditions that seriously impact the normal functioning of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two well known types of VA. VF is the lethal of the VAs and could be characterized by its organizational progression over time. The success of cardiac resuscitation strongly depends on the type of VA, its evolution over time and response to therapy. Due to the time critical nature of VF, computationally efficient quantification of VAs and swift feedback are essential. This work attempted to arrive at computationally efficient and data-driven techniques based on Empirical Mode Decomposition for classifying and tracking VAs over time. The approaches are divided into two aims: (1) 'in-hospital' scenarios for characterizing the dynamics of VA episodes to assist clinicians in planning long-term therapy options, and (2) 'out-of-hospital' scenarios for providing near real-time feedback to detect/track the progression of VAs over time to assist medical personnel select/modify therapy options. Using an ECG database of 61 60-s VA segments obtained for classifying VT vs. VF and sub-classifying VF into organized VF (OVF) and disorganized VF (DVF), maximum classification accuracies of 96.7% (AUC = 0.993) and 87.2% (AUC = 0.968) were obtained for classifying VT vs. VF and OVF vs. DVF during 'in-hospital' analysis. Additionally, two near real-time approaches were presented for 'out-of-hospital' analysis where average accuracies of 71% and 73% were achieved for VT/VF and OVF/DVF classification, as well as demonstrating strong potential for monitoring VA progressions over time.

Baseline characteristics and clinical symptoms related to respiratory viruses identified among patients presenting with influenza-like illness in primary care.

OBJECTIVES: We aimed to identify patients' clinical characteristics associated with respiratory viruses identified among patients presenting with influenza-like illness (ILI). METHODS: A sample of patients of all ages presenting with ILI was included by physicians of the French Sentinelles network during two seasons (2015/16 and 2016/17). Nasopharyngeal samples were tested for the presence of influenza virus (IV), respiratory syncytial virus (RSV), human rhinovirus (HRV) and human metapneumovirus (HMPV). Patients' characteristics associated with each of the four virus classes were studied using multivariate logistic regressions. RESULTS: A total of 5859 individuals were included in the study: 48.0% tested positive for IV, 7.9% for HRV, 7.5% for RSV and 4.1% for HMPV. Cough was associated with IV (OR 2.14, 95% CI 1.81-2.52) RSV (OR 2.52, 95% CI 1.75-3.74) and HMPV detection (OR 2.15, 95% CI 1.40-3.45). Rhinorrhoea was associated mainly with HRV detection (OR 1.75, 95% CI 1.34-2.32). Headache was associated with IV detection (OR 1.75, 95% CI 1.34-2.32), whereas absence of headache was associated with RSV and HMPV detection. Dyspnoea was associated with RSV detection (OR 2.33, 95% CI 1.73-3.12) and absence of dyspnoea with IV detection. Conjunctivitis was associated with IV detection (OR 1.27, 95% CI 1.08-1.50). Some associations were observed only in children: dyspnoea and cough with RSV detection (age <5 years), conjunctivitis with IV detection (age <15 years). Period of onset of symptoms differed among aetiological diagnoses. Seasonal influenza vaccination decreased the risk of IV detection (OR, 0.67, 95% CI 0.51-0.86). CONCLUSIONS: This study allowed the identification of symptoms associated with several viral aetiologies in patients with ILI. A proper knowledge and understanding of these clinical signs may improve the medical management of patients.

Molecular investigation of tick-borne pathogens in ixodid ticks infesting domestic animals (cattle and sheep) and small rodents (black rats) of Corsica, France.

Although livestock farming (sheep, goats, pigs, and cattle) is an important economic activity in Corsica, a French Mediterranean island, knowledge about the tick fauna and microorganisms carried by them remains scarce. This study aimed to investigate the presence and perform molecular characterization of Anaplasmataceae, Rickettsia spp., and Borrelia burgdorferi sensu lato (sl) in tick species collected in Corsica. Ticks from cattle (Bos taurus), sheep (Ovis aries), and rodents (Rattus rattus) were collected from May to September 2016. DNA was purified from ticks, submitted to quantitative real-time polymerase chain reaction (qPCR) and sequenced for phylogenetic analysis. In total, 660 ticks were collected from 111 animals during the study. The most abundant collected tick species from cattle was Rhipicephalus bursa (n = 495; 84.5%), followed by Hyalomma marginatum (n = 91; 15.5%). Rhipicephalus bursa and Ixodes ricinus were the only tick species collected from sheep and rodents, respectively. Overall, Rickettsia was the most common pathogen group (n = 48; 24%) detected in ticks. Sequence analysis of partial gltA and ompA genes revealed the presence of Ri. aeschlimannii and Candidatus Ri. barbariae. Anaplasmataceae DNA was detected in eight (6%) of the 127 cattle pools and in one (2%) of the 61 R. bursa specimens collected from sheep. Sequence analysis of the rpoB gene revealed the presence of one Anaplasma species, A. marginale. Borrelia burgdorferi sl DNA was detected in one pool of H. marginatum collected from cattle and in two (15%) of the 13 I. ricinus pools collected from nine black rats. To our knowledge, this is the first report of the occurrence and molecular characterization of Candidatus Ri. barbariae, an emerging member of the Rickettsia group causing spotted fever, in Corsica. The detection of B. burgdorferi sl DNA, which was previously believed to be rare in Corsica, confirms the presence of this agent on the island.

Estimation of seasonal influenza vaccine effectiveness using data collected in primary care in France: comparison of the test-negative design and the screening method.

OBJECTIVES: We discussed which method between the test-negative design (TND) and the screening method (SM) could provide more robust real-time and end-of-season vaccine effectiveness (VE) estimates using data collected from routine influenza surveillance in primary care. METHODS: We used data collected during two influenza seasons, 2014-15 and 2015-16. Using the SM, we estimated end-of-season VE in preventing medically attended influenza-like illness and laboratory-confirmed influenza among the population at risk. Using the TND, we estimated end-of-season VE in preventing influenza among both the general and the at-risk population. We estimated real-time VE using both methods. RESULTS: For the SM, the overall adjusted end-of-season VE was 24% (95% confidence interval (CI), 16 to 32) and 12% (95% CI, -16 to 33) during season 2014-15, and 53% (95% CI, 44 to 60) and 47% (95% CI, 23 to 64) during season 2015-16, in preventing influenza-like illness and laboratory-confirmed influenza, respectively. For the TND, the overall adjusted end-of-season VE was -17% (95% CI, -79 to 24) and -38% (95% CI, -199 to 13) in 2014-15, and 10% (95% CI, -31 to 39) and 18% (95% CI, -33 to 50) in 2015-16, among the general and at-risk population, respectively. Real-time VE estimates obtained through the TND showed more variability across each season and lower precision than those estimated with the SM. CONCLUSIONS: Although the worldwide use of the TND allows for comparison of overall VE estimates among countries, the SM performs better in providing robust real-time VE estimates among the population at risk.

Performances of statistical methods for the detection of seasonal influenza epidemics using a consensus-based gold standard.

Influenza epidemics are monitored using influenza-like illness (ILI) data reported by health-care professionals. Timely detection of the onset of epidemics is often performed by applying a statistical method on weekly ILI incidence estimates with a large range of methods used worldwide. However, performance evaluation and comparison of these algorithms is hindered by: (1) the absence of a gold standard regarding influenza epidemic periods and (2) the absence of consensual evaluation criteria. As of now, performance evaluations metrics are based only on sensitivity, specificity and timeliness of detection, since definitions are not clear for time-repeated measurements such as weekly epidemic detection. We aimed to evaluate several epidemic detection methods by comparing their alerts to a gold standard determined by international expert consensus. We introduced new performance metrics that meet important objective of influenza surveillance in temperate countries: to detect accurately the start of the single epidemic period each year. Evaluations are presented using ILI incidence in France between 1995 and 2011. We found that the two performance metrics defined allowed discrimination between epidemic detection methods. In the context of performance detection evaluation, other metrics used commonly than the standard could better achieve the needs of real-time influenza surveillance.

Influenza-like illness outbreaks in nursing homes in Corsica, France, 2014-2015: epidemiological and molecular characterization.

BACKGROUND: To study the molecular epidemiology of the influenza outbreaks in nursing homes (NHs) to determine whether multiple influenza strains were involved. METHODS: From September to December 2014, NHs in Corsica were invited to participate in an ongoing daily epidemiological and microbiological surveillance for influenza-like illness (ILI) among residents and health care workers (HCWs). RESULTS: The study involved 12 NHs. Respiratory illness meeting the ILI case definition was observed among 44 residents from whom 22 specimens were collected. Of the 22 residents with a nasopharyngeal sample, 13 (59 %) were positive for at least one of the 11 pathogens analysed. Among these 13 patients, 11 (92 %) presented a confirmed influenza (A/H3N2) and two had another respiratory virus: one human metapneumovirus and one human coronavirus. Of patients with a confirmed influenza A(H3N2), 10 (91 %) were vaccinated against influenza during the 2014-2015 season. Two influenza outbreaks were reported in two NHs, caused by influenza A(H3N2) strains belonging to cluster 3C.3 and 3C.2a. Although antivirals were available, prophylaxis was not used. CONCLUSIONS: Phylogenetic analysis seems to suggest no multiple introduction into the two NHs reporting the two influenza A(H3N2) outbreaks. A number of factors could have contributed to transmitting influenza in NHs including, the absence of administration of antiviral treatment for prophylaxis of all residents/staff regardless of immunization status because of the poor vaccine match during each outbreak, the intensive contacts with incompletely protected residents and HCWs, and the low adherence of NHs to notification of ILI outbreaks to the health authorities.

Modeling Current Density Maps Using Aliev-Panfilov Electrophysiological Heart Model.

Most existing studies of cardiac arrhythmia rely on surface measurements through optical or electrical mapping techniques. Current density imaging (CDI) is a method which enables us to study current pathways inside the tissue. However, this method entails implementation complexities for beating ex vivo hearts. Hence, this work presents an approach to simulate and study the current distributions in different cardiac electrophysiological states. The results are corroborated by experimental data, and they indicate that different states were distinguishable. The CDI simulations can be used for studying cardiac arrhythmias under simulation conditions which are otherwise impossible or difficult to be implemented experimentally.

Parameters influencing ciprofloxacin, ofloxacin, amoxicillin and sulfamethoxazole retention by natural and converted calcium phosphates.

The retention of four antibiotics, ciprofloxacin, ofloxacin, amoxicillin and sulfamethoxazole by a natural phosphate rock (francolite) was studied and compared with a converted hydroxyapatite powder. The maximum sorption capacities were found to correlate with the molecular weight of the molecules. The mechanisms of sorption depended mostly on the charge of the antibiotic whereas the kinetics of the process was sensitive to their hydrophobic/hydrophilic character. The two materials showed slightly distinct affinities for the various antibiotics but exhibited similar maximum sorption capacities despite different specific surface areas. This was mainly attributed to the more pronounced hydrophobic character of the francolite phase constituting the natural phosphate. These data enlighten that the retention properties of these mineral phases depend on a complex interplay between the inter-molecular and molecule-solid interactions. These findings are relevant to understand better the contribution of calcium phosphates in the fate and retention of antibiotics in soils.

The effects of long chain polyunsaturated fatty acids on local activation properties in dogs vulnerable to atrial fibrillation.

Marine derived long chain polyunsaturated fatty acids (PUFAs) were found to have benefits in reducing inducibility and maintenance of atrial fibrillation (AF) in a dog model. This study was conducted to evaluate the effect of PUFAs on local atrial electrical conduction properties acquired via a multi-electrode plaque sutured to the posterior wall of the left atrium of the heart in these dogs. Eleven dogs underwent simultaneous atrioventricular pacing (SAVP) for 2 weeks, and were organized into 2 groups: 5 dogs received no PUFAs (SAVP-PLACEBO), 6 dogs received Eicosapentaenoic or Docosahexaenoic acid derived from fish oils (SAVP-PUFA), where PUFAs were given for 21 days, starting 1 week prior to pacing and during the 2 week pacing period. Three features were extracted, which were the average conduction velocity, average intra atrial conduction time, and total activation time. The PUFA group had a faster average conduction velocity (0.82±0.19 m/s) than the PLACEBO group (0.47±0.21 m/s, P=0.02). Using the average conduction velocity feature, classification was performed with a linear classifier and leave-one-out method. In the SAVP-PLACEBO group, 60% of the dogs were correctly classified, and 66% of the dogs were correctly classified in SAVP-PUFA group, leading to an overall classification accuracy of 63.5%.

A novel approach to quantification of real and artifactual components of current density imaging for phantom and live heart.

Spatial distribution of injected current in a subject could be calculated and visualized through current density imaging (CDI). Calculated CDI paths however have a limited degree of accuracy due to both avoidable methodological errors and inevitable limitations dictated by MR imaging constraints. The source and impact of these limitations are scrutinized in this paper. Quantification of such limitations is an essential step prior to passing any judgment about the results especially in biomedical applications. An innovative technique along with metrics for evaluation of range of errors using baseline and phase cycle MR images is proposed in this work. The presented approach is helpful in pinpointing the local artifacts (areas for which CDI results are suspect), evaluation of global noises and artifacts and assessment of the effect of approximation algorithms on real and artifactual components. We will demonstrate how this error/reliability evaluation is applicable to interpretation of CDI results and in this framework, report the CDI results for an artificial phantom and a live pig heart in Langendorff setup. It is contended here that using this method, the inevitable trade-off between details and approximations of CDI components could be monitored which provides a great opportunity for robust interpretation of results. The proposed approach could be extended, adapted and used for statistical analysis of similar methods which aim at mapping current and impedance based on magnetic flux images obtained through MRI.

Fusion of structural and functional cardiac magnetic resonance imaging data for studying ventricular fibrillation.

Magnetic Resonance Imaging (MRI) techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) provide a complementing set of imaging data that can describe both the functional and structural states of biological tissues. This paper presents a Joint Independent Component Analysis (jICA) based fusion approach which can be utilized to fuse CDI and DTI data to quantify the differences between two cardiac states: Ventricular Fibrillation (VF) and Asystolic/Normal (AS/NM). Such an approach could lead to a better insight on the mechanism of VF. Fusing CDI and DTI data from 8 data sets from 6 beating porcine hearts, in effect, detects the differences between two cardiac states, qualitatively and quantitatively. This initial study demonstrates the applicability of MRI-based imaging techniques and jICA-based fusion approach in studying cardiac arrhythmias.

Automated signal pattern detection in ECG during human ventricular arrhythmias.

Ventricular arrhythmias seriously affects cardiac function. Of these arrhythmias, Ventricular fibrillation is considered as a lethal cardiac condition. Recent studies have reported that ventricular arrhythmias are not completely random and may exhibit regional spatio-temporal organizations. These organizations could be indicative of reoccurring signal patterns and might be embedded within the surface electrocardiograms (ECGs) during ventricular arrhythmias. In this work, we aim to identify such reoccurring ECG signal patterns during ventricular arrhythmias. The detection of such signal patterns and their distribution could be of help in sub-classifying the affected population for better targeted diagnosis and treatment. Our analysis on 14 ECG segments (on average 3.24 minutes per segment) obtained from the MIT-BIH ventricular arrhythmia database identified three reoccurring signal patterns. A wavelet based technique was developed for automating the pattern identification process using ECGs. The proposed method achieved automated detection accuracies of 73.3%, 75.0% and 86.6% for the proposed signal patterns.

Analysis of reliability metrics and quality enhancement measures in current density imaging.

Low frequency current density imaging (LFCDI) is a magnetic resonance imaging (MRI) technique which enables calculation of current pathways within the medium of study. The induced current produces a magnetic flux which presents itself in phase images obtained through MRI scanning. A class of LFCDI challenges arises from the subject rotation requirement, which calls for reliability analysis metrics and specific image registration techniques. In this study these challenges are formulated and in light of proposed discussions, the reliability analysis of calculation of current pathways in a designed phantom and a pig heart is presented. The current passed is measured with less than 5% error for phantom, using CDI method. It is shown that Gauss's law for magnetism can be treated as reliability metric in matching the images in two orientations. For the phantom and pig heart the usefulness of image registration for mitigation of rotation errors is demonstrated. The reliability metric provides a good representation of the degree of correspondence between images in two orientations for phantom and pig heart. In our CDI experiments this metric produced values of 95% and 26%, for phantom, and 88% and 75% for pig heart, for mismatch rotations of 0 and 20 degrees respectively.

Blind source separation in characterizing ECG pre-shock waveforms during Ventricular Fibrillation.

Ventricular Fibrillation (VF) is a cardiac arrhythmia for which the only available treatment option is defibrillation by electrical shock. Existing literature indicates that VF could be the manifestation of different sources controlling the heart with different degrees of organization. In this work we test the hypothesis that the pre-shock waveforms of successful and unsuccessful shock outcomes could be related to the number of independent sources present in these waveforms. The proposed method uses Blind Source Separation (BSS) to extract independent components in frequency direction from a pig database consisting of 20 pre-shock waveforms. The slope of the energy capture curve was used as an indicator to demonstrate the number of independent sources required to model the pre-shock waveforms. The results were also quantified by performing a linear discriminant analysis based classification achieving an overall classification accuracy of 75%. The results indicate that successful cases can be modeled with less number of independent sources compared to unsuccessful cases.

A classification scheme for ventricular arrhythmias using wavelets analysis.

Identification and classification of ventricular arrhythmias such as rhythmic ventricular tachycardia (VT) and disorganized ventricular fibrillation (VF) are vital tasks in guiding implantable devices to deliver appropriate therapy in preventing sudden cardiac deaths. Recent studies have shown VF can exhibit strong regional organizations, which makes the overlap zone between the fast paced rhythmic VT and VF even more ambiguous. Considering that implantable cardioverter-defibrillator (ICD) are primarily rate dependent detectors of arrhythmias and that there may be patients who suffer from arrhythmias that fall in the overlap zone, it is essential to identify the degree of affinity of the arrhythmia toward VT or organized/disorganized VF. The method proposed in this work better categorizes the overlap zone using Wavelet analysis of surface ECGs. Sixty-three surface ECG signal segments from the MIT-BIH database were used to classify between VT, organized VF (OVF), and disorganized VF (DVF). A two-level binary classifier was used to first extract VT with an overall accuracy of 93.7% and then the separation between OVF and DVF with an accuracy of 80.0%. The proposed approach could assist clinicians to provide optimal therapeutic solutions for patients in the overlap zone of VT and VF.

Studying semblances of a true killer: experimental model of human ventricular fibrillation.

It is unknown whether ventricular fibrillation (VF) studied in experimental models represents in vivo human VF. First, we examined closed chest in vivo VF induced at defibrillation threshold testing (DFT) in four patients with ischemic cardiomyopathy pretransplantation. We examined VF in these same four hearts in an ex vivo human Langendorff posttransplantation. VF from DFT was compared with VF from the electrodes from a similar region in the right ventricular endocardium in the Langendorff using two parameters: the scale distribution width (extracted from continuous wavelet transform) and VF mean cycle length (CL). In a second substudy group where multielectrode phase mapping could be performed, we examined early VF intraoperatively (in vivo open chest condition) in three patients with left ventricular cardiomyopathy. We investigated early VF in the hearts of three patients in an ex vivo Langendorff and compared findings with intraoperative VF using two metrics: dominant frequency (DF) assessed by the Welch periodogram and the number of phase singularities (lasting >480 ms). Wavelet analysis (P = 0.9) and VF CL were similar between the Langendorff and the DFT groups (225 ± 13, 218 ± 24 ms; P = 0.9), indicating that wave characteristics and activation rate of VF was comparable between the two models. Intraoperative DF was slower but comparable with the Langendorff DF over the endocardium (4.6 ± 0.1, 5.0 ± 0.4 Hz; P = 0.9) and the epicardium (4.5 ± 0.2, 5.2 ± 0.4 Hz; P = 0.9). Endocardial phase singularity number (9.6 ± 5, 12.1 ± 1; P = 0.6) was lesser in number but comparable between in vivo and ex vivo VF. VF dynamics in the limited experimental human studies approximates human in vivo VF.

Relating spatial heterogeneities to rotor formation in studying human ventricular fibrillation.

Ventricular fibrillation (VF) occurs due to disorganized electrical activity in the ventricles. This leads to rapid uncoordinated contractions of the ventricles and sudden cardiac death if not treated within minutes of its occurrence. The mechanism of VF initiation and maintenance is still elusive, however the mother rotor and multiple wavelet theories attempt to explain the mechanism behind this lethal arrhythmia. In mother rotor theory, VF is believed to be maintained by high frequency periodic sources called rotors that could be tracked using the phase progression along and through the myocardium using spatio-temporal electrical mapping of the heart. There are exiting works including our previous works that have related the formation of these rotors to anatomical and physiological heterogeneities observed in the myocardium. In this study we performed an correlation exercise of the locations of rotors with scar boundary maps and dominant frequency maps and elucidated this relation using human VF data acquired from isolated human hearts. The results suggest that in 14 rotors over 6 human hearts that we studied, all rotors co-localized to boundary zones of scar and low-high dominant frequency locations. The mean variance of the dominant frequency over the spatial location of the rotor was found to be 0.55 with average minimum of 4.15 Hz to a maximum of 5.71 Hz. This results in human VF data strongly suggest that boundary zones of healthy-non-healthy tissues and low-high frequency boundaries form a favorite substrate for rotor formation.

Predicting refibrillation from pre-shock waveforms in optimizing cardiac resuscitation.

Ventricular fibrillation (VF) is a lethal cardiac arrhythmia that if untreated within minutes of its occurrence will lead to sudden cardiac death. Defibrillation using electric shocks is the only choice of treatment to restore the heart to normal rhythm especially in out-of-the-hospital VF incidents. Refibrillation (i.e., recurrence of VF) is a common and significant problem in cardiac resuscitation as it negatively impacts the survival rates. In such refibrillation cases administration of anti-arrhythmic drugs could improve the shock outcomes or prevent refibrillation. In cases of prolonged VF, cardio pulmonary resuscitation (CPR) prior to the shocks have been shown to improve the survival rates. The proposed work using wavelet analysis of the pre-shock VF electrograms attempts to predict the shock outcomes as successful, refibrillation, and unsuccessful categories. This feedback in real-time would be of immense assistance to the Emergency Medical Services (EMS) personnel in choosing the right combination of therapies (i.e., shock, CPR, pharmacology interventions) in improving the shock outcomes. Using a real-word database of 34 pre-shock VF electrograms obtained from Toronto area EMS personnel, the proposed method achieved classification accuracies of 76.5% and 75% for a two level binary classification of the three groups.

Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options.

Ventricular arrhythmias arise from abnormal electrical activity of the lower chambers (ventricles) of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two major subclasses of ventricular arrhythmias. While VT has treatment options that can be performed in catheterization labs, VF is a lethal cardiac arrhythmia, often when detected the patient receives an implantable defibrillator which restores the normal heart rhythm by the application of electric shocks whenever VF is detected. The classification of these two subclasses are important in making a decision on the therapy performed. As in the case of all real world process the boundary between VT and VF is ill defined which might lead to many of the patients experiencing arrhythmias in the overlap zone (that might be predominately VT) to receive shocks by the an implantable defibrillator. There may also be a small population of patients who could be treated with anti-arrhythmic drugs or catheterization procedure if they can be diagnosed to suffer from predominately VT after objectively analyzing their intracardiac electrogram data obtained from implantable defibrillator. The proposed work attempts to arrive at a quantifiable way to scale the ventricular arrhythmias into VT, VF, and the overlap zone arrhythmias as VT-VF candidates using features extracted from the wavelet analysis of surface electrograms. This might eventually lead to an objective way of analyzing arrhythmias in the overlap zone and computing their degree of affinity towards VT or VF. A database of 24 human ventricular arrhythmia tracings obtained from the MIT-BIH arrhythmia database was analyzed and wavelet-based features that demonstrated discrimination between the VT, VF, and VT-VF groups were extracted. An overall accuracy of 75% in classifying the ventricular arrhythmias into 3 groups was achieved.