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Background: The aim of this study was to achieve a consensus on the minimum set of outcome measures and predictors to be used in the neurosurgical practice and on the timing of outcome assessment. Methods: A consensus building approach was employed. All neurosurgical departments in Lombardy (Italy) were invited to participate by the Carlo Besta Neurologic Institute IRCCS Foundation. Three workshops were organized during which a multidisciplinary group called Neurosurgical Outcome Network (NEON) was created and the methodology to select outcome measures, predictors, and timing of outcome assessment was established. Eight working groups were created for the different neurosurgical diseases (neuro-oncological, skull base, vascular, traumatic, spinal, peripheral nervous system, malformation, functional) and 8 workshops were organized to identify the outcome measures and predictors specific for each of the neurosurgical diseases based on the experts' clinical practice and the existing literature. Results: A total of 20 neurosurgical departments participated in this study. Specific outcome measures, predictors and the timing of outcome assessment were identified for each of the 8 neurosurgical diseases. Moreover, a list of variables common to all pathologies were identified by the NEON group as further data to be collected. Conclusions: A consensus on the minimum set of outcome measures and predictors and the timing of outcome assessments for 8 neurosurgical diseases was achieved by a group of neurosurgeons of the Lombardy region, called NEON. These sets could be used in future studies for a more homogeneous data collection and as a starting point to reach further agreement also at national and international level.

ectorcardiogram (VCG) has been repeatedly found useful for clinical investigations. It may not substitute but complement Standard 12-Lead (S12) ECG. There was tremendous research between 1950s to mid-1980s on VCG in general and Frank’s System in particular, however, in last three decades it has been dropped as a routine cardiac test. The major reasons being unconventional electrode placements which required training of the physicians, greater number of electrodes involved when used to supplement S12 system and additional hardware complexity involved, at least in the early days. Although it lost the interest of cardiologists, the engineering community has adopted the VCG as a tool for interdisciplinary research. We envisage that, if accurate Frank’s VCG system is made available avoiding the aforementioned limitations, VCG will complement S12 system in diagnosis of cardiovascular diseases (CVD).

In this article, we have proposed a robust and accurate method for the reconstruction of standard 12-lead (S12) system from Frank vectorcardiographic (FV) system using personalised transformation (PT) matrices targeting personalised remote health monitoring applications. FV system is used in the 3D visualisation of heart and diagnosis and prognosis of many cardiologic disorders including myocardial infarction, Brugada syndrome etc. However, cardiologists are accustomed to S12 system pertaining to its decades-old usage and widespread acceptability and hence, it is generally used as primary ECG acquisition system and state-of-the-art inverse Dower transform (DT) and affine transform (AT) are used to obtain FV system from S12 system. Here, we propose the acquisition of FV system and use PT to reconstruct S12 system from FV system. PhysioNet's PTB database after wavelet-based preprocessing to remove baseline wandering and noise has been used in this investigation. The personalised coefficients have been obtained using least-squares fit method and heart-vector projection theory and evaluation metrics used are R-2 statistics, correlation and regression coefficients. Our proposed PT methodology has outperformed AT and DT by mean R-2 values of 16.36% and 26.53%, respectively. For the practical application of the proposed system, we have investigated into the reusability of personalised coefficients which has been shown to outperform state-of-the-art AT and DT.

Remote Health Monitoring (RHM) applications encounter limitations from technological front viz. bandwidth, storage and transmission time and the medical science front i.e. usage of 2-3 lead systems instead of the standard 12-lead (S12) system. Technological limitations constraint the number of leads to 2-3 while cardiologists accustomed with 12-Lead ECG may find these 2-3 lead systems insufficient for diagnosis. Thus, the aforementioned limitations pose self-contradicting challenges for RHM. A personalized reduced 2/3 lead system is required which can offer equivalent information as contained in S12 system, so as to accurately reconstruct S12 system from reduced lead system for diagnosis. In this paper, we propose a personalized reduced 3-lead (R3L) system formation methodology which employs principal component analysis, thereby, reducing redundancy and increasing SNR ratio, hence, making it suitable for wireless transmission. Accurate S12 system is made available using personalized lead reconstruction methodology, thus addressing medical constraints. Mean R2 statistics values obtained for reconstruction of S12 system from the proposed R3L system using PhysioNet's PTB and TWA databases were 95.63% and 96.37% respectively. To substantiate the superior diagnostic quality of reconstructed leads, root mean square error (RMSE) metrics obtained upon comparing the ECG features extracted from the original and reconstructed leads, using our recently proposed Time Domain Morphology and Gradient (TDMG) algorithm, have been analyzed and discussed. The proposed system does not require any extra electrode or modification in placement positions and hence, can readily find application in computerized ECG machines.

Cardiovascular diseases (CVD) are the prime causes of human mortality and morbidity worldwide. However, CVD can be prevented or cured, if detected early or on-time, where technology can be of significant help. Tackling the issue of comfort of patient by reducing the number of electrode, by allowing the remote home monitoring, and by allaying the need of physical presence of patient in hospital is the prime focus. Here, ECG monitoring system has been discussed to increase the comfort level of patient by reducing the number of electrodes and supporting remote health monitoring. In this paper we implement a low complexity real time system to de-noise and reconstruct a standard 12-lead ECG from reduced 3-lead ECG using both hardware and software platform. De-noising is composed of baseline wandering removal, which has been prototyped on VC707 and ATLYS (Spartan 6) evaluation kit and 3-lead to 12-lead signal reconstruction implemented on android platform for mobile devices. The proposed system reconstructs S12 ECG accurately using personalized transformations. PhysioNet's PTBDB has been used for validation. The system generated outputs diagnostic accuracy has been endorsed by two experienced cardiologists.

Remote areas, generally, encounter scarcity of cardiologists and state-of-the-art facilities for the treatment of cardiovascular diseases (CVD). However, aforementioned constraints can be addressed using recent advancements in wireless technology and its omnipresence. Clinical CVD diagnosis is, generally, carried out using either of Standard 12-Lead (S12) or Mason-Likar 12-Lead (ML12) system. These systems consist of 8 independent leads/signals which restrict their usage in telemonitoring applications viz. personalized remote health monitoring, home monitoring etc., due to high bandwidth and storage requirements, data transmission time and low compression ratio (CR) from signal compression techniques. Moreover, the reduced lead (RL) systems with 2-3 leads, generally, employed in telemonitoring applications might not be sufficient for diagnosis. We form a Reduced 3-Lead system from S12 and ML12 systems at the transmission end which reduces the number of signals to 3 and then reconstruct the S12 and ML12 systems at the receiver end using our proposed personalized reconstruction methodology, thus allaying aforementioned limitations. I, II and V2 form the basis leads and the precordial leads form the target leads. Least square fit and heart vector projection theory have been used to obtain personalized transformation coefficients. Accuracy of reconstruction has been evaluated on PhysioNet PTBDB and INCARTDB, after wavelet based preprocessing, using R2 statistics, correlation (rx) and regression (bx) coefficients. Re-usability of personalized coefficients has also been investigated in this paper. Mean R2 values obtained from the reconstruction of target leads are 91.87% (PTBDB) 83.75% (INCARTDB). R3L system reduces the number of leads/signals from 8 to 3 and as the results indicate the possibility of re-using the transformation coefficients, the number of electrodes can be reduced from 10 to 5, thereby, increasing the comfort of patients and caregivers.

Fragmented QRS (f-QRS) has been found to have higher sensitivity and/or specificity values for several diseases including remote and acute myocardial infarction, cardiac sarcoidosis etc, compared to other conventional bio-markers viz. Q-wave, ST-elevation etc. Several of these diseases do not have a reliable bio-marker and hence, patients suffering from them have to undergo expensive and sometimes invasive tests for diagnosis viz. myocardial biopsy, cardiac catheterization etc. This paper proposes automation of fragmentation detection which will lead to a more reliable diagnosis and therapy reducing human error, time consumption and thereby alleviating the need of enormous training required for detection of fragmentation. In this paper, we propose a novel approach to detect the discontinuities present in QRS complex of standard 12-lead ECG, known as fragmented QRS, using Discrete Wavelet transform (DWT) targeting both hospital-based and remote health monitoring environments. Fragmentation Detection Algorithm (FDA) was designed and modeled using PhysioNet's PTBDB and upon reiterative refinements it successfully detected all discontinuities in the QRS complex. The QRS complexes of 31 patients obtained randomly from PhysioNet's PTBDB were examined by two experienced cardiologists and the measurements obtained were compared with the results of our proposed FDA leading to 89.8% agreement among them

Fragmented QRS (f-QRS) has been proven to be an efficient biomarker for several diseases, including remote and acute myocardial infarction, cardiac sarcoidosis, non-ischaemic cardiomyopathy, etc. It has also been shown to have higher sensitivity and/or specificity values than the conventional markers (e.g. Q-wave, ST-elevation, etc.) which may even regress or disappear with time. Patients with such diseases have to undergo expensive and sometimes invasive tests for diagnosis. Automated detection of f-QRS followed by identification of its various morphologies in addition to the conventional ECG feature (e.g. P, QRS, T amplitude and duration, etc.) extraction will lead to a more reliable diagnosis, therapy and disease prognosis than the state-of-the-art approaches and thereby will be of significant clinical importance for both hospital-based and emerging remote health monitoring environments as well as for implanted ICD devices. An automated algorithm for detection of f-QRS from the ECG and identification of its various morphologies is proposed in this work which, to the best of our knowledge, is the first work of its kind. Using our recently proposed time-domain morphology and gradient-based ECG feature extraction algorithm, the QRS. © 2013 The Author(s) Published by the Royal Society. All rights reserved.

Object: Incident reporting systems are universally recognized as important tools for quality improvement in all complex adaptive systems, including the operating room. Nevertheless, introducing a safety culture among neurosurgeons is a slow process, and few studies are available in the literature regarding the implementation of an incident reporting system within a neurosurgical department. The authors describe the institution of an aviation model of incident reporting and investigation in neurosurgery, focusing on the method they have used and presenting some preliminary results.Methods: In 2010, the Inpatient Safety On-Board project was developed through cooperation between a team of human factor and safety specialists with aviation backgrounds (DgSky team) and the general manager of the Fondazione IstitutoNeurologico Carlo Besta. In 2011, after specific training in safety culture, the authors implemented an aviation-derived prototype of incident reporting within the Department of Neurosurgery. They then developed an experimental protocol to track, analyze, and categorize any near misses that happened in the operating room. This project officially started in January 2012, when a dedicated team of assessors was established. All members of the neurosurgical department were asked to report near misses on a voluntary, confidential, and protected form (Patient Incident Reporting System form, Besta Safety Management Programme). Reports were entered into an online database and analyzed by a dedicated team of assessors with the help of a facilitator, and an aviation-derived root cause analysis was performed.Results: Since January 2012, 14 near misses were analyzed and classified. The near-miss contributing factors were mainly related to human factors (9 of 14 cases), technology (1 of 14 cases), organizational factors (3 of 14 cases), or procedural factors (1 of 14 cases).Conclusions: Implementing an incident reporting system is quite demanding; the process should involve