Software code complexity assessment using EEG features

This paper provides a study using Electroencephalography (EEG) to investigate the brain activity during code comprehension tasks. Three different code complexity levels according to five complexity metrics were considered. The use of EEG for this purpose is relevant, since the existing studies were mostly focused on neuroimaging techniques. Using Leave-One-Subject-Out cross-validation procedure for 30 subjects, it was found that the features related with the Gamma activity were the most common in all the folds. Regarding the brain regions, right parietal was the most frequent region contributing with more features. A Linear Discriminant Analysis Classifier for task classification, obtained a F-Measure of 92.71% for Code complexity easy, 52.25% for Code complexity intermediate and 53.13% for Code complexity advanced, revealing an evidence of mental effort saturation with the code complexity degree. This suggests that current code complexity metrics do not capture cognitive load and might not be the best approach to assess bug risk.

Physiologically motivated detection of Atrial Fibrillation

Atrial Fibrillation (AF) is the most common arrhythmia and it is estimated to affect 33.5 million people worldwide. AF is associated with an increased risk of mortality and morbidity, such as heart failure and stroke and affects mostly older persons and persons with other conditions (e.g. heart failure and coronary artery disease). In order to prevent such life threatening and life quality reducing conditions it is essential to provide better algorithms, capable of being integrated in low-cost personalized health systems. This paper presents a new algorithm for AF detection, which is based on the analysis of the three physiological characteristics of AF: 1) Irregularity of heart rate and; 2) Absence of P-waves and 3) Presence of fibrillatory waves. Based on these characteristics several features were extracted from 12-lead electrocardiograms (ECG) and selected according to their discrimination ability. The classification between AF and non-AF episodes was performed using a Support Vector Machine (SVM) classification model. Our results show that the identification of the fibrillatory patterns, using the proposed features, extracted from the analysis of 12-lead ECG improves the performance of the algorithm to a sensitivity of 88.5% and specificity 92.9%, when compared to our previous single-channel approach, in the same database.

Macular choroidal thickness after vitreoretinal surgery: Long-term effect of pars plana vitrectomy with and without encircling scleral buckling surgery

PURPOSE

To evaluate the macular choroidal thickness (CT) of eyes subjected to pars plana vitrectomy (PPV) whether or not combined with encircling scleral buckling (ESB) surgery for primary rhegmatogenous retinal detachment repair at 6 months or more after surgery.

METHODS

This observational study included: 15 eyes (15 patients) submitted to combined ESB+PPV; 15 eyes submitted to PPV and their respective 30 normal fellow eyes (FE). Two 6mm lineal perpendicular optical coherence tomography B-scans centred on the fovea with enhanced depth imaging were performed on each eye. CT was measured at several macular locations: subfoveal (SF-CT) and at a radius of 1, 2, and 3mm from the fovea. CTs of the eyes in the CE+PPV group were compared to CT in the PPV group and the CTs of all operated eyes were compared to the CTs of their FE.

RESULTS

SF-CT of the eyes in the ESB+PPV group was significantly increased compared to their FE (P=.001). CT at a radius of 1, 2, and 3mm from the fovea of the ESB+PPV group were significantly increased (P=.001, P=.005, and P=.001, respectively). The SF-CT of the PPV group was similar to their FE (P=.691). The SF-CT of the ESB+PPV group was significantly increased compared to SF-CT of the PPV group (P=.019).

CONCLUSIONS

The CT of the eyes subjected to combined ESB and PPV was significantly increased at 6 months or more after surgery compared to the CT of their FE and to the CT of the eyes subjected to PPV alone, which could be explained by a venous engorgement caused by the ESB.

Detection of explosive cough events in audio recordings by internal sound analysis

We present a new method for the discrimination of explosive cough events, which is based on a combination of spectral content descriptors and pitch-related features. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 9 features is extracted for each event. Two data sets, recorded using electronic stethoscopes and comprising a total of 46 healthy subjects and 13 patients, were employed to evaluate the method. The proposed feature set is compared to three other sets of descriptors: a baseline, a combination of both sets, and an automatic selection of the best 10 features from both sets. The combined feature set yields good results on the cross-validated database, attaining a sensitivity of 92.3±2.3% and a specificity of 84.7±3.3%. Besides, this feature set seems to generalize well when it is trained on a small data set of patients, with a variety of respiratory and cardiovascular diseases, and tested on a bigger data set of mostly healthy subjects: a sensitivity of 93.4% and a specificity of 83.4% are achieved in those conditions. These results demonstrate that complementing the proposed feature set with a baseline set is a promising approach.

Artifact detection in accelerometer signals acquired from the carotid

Manual pulse palpation is the common procedure to assess pulse in unconscious patients. This is an error prone procedure during cardiopulmonary resuscitation and therefore automatic pulse detection techniques are being investigated. Accelerometry is an interesting sensing modality for this type of applications. However, accelerometers are highly prone to movement artifacts. Hence, one challenge in designing a solution using accelerometers is to handle motion artifacts properly. In this paper we investigate computationally simple features and classifier to capture movement artifacts in accelerometer signals acquired from the carotid. In particular, based on data obtained from health subjects we show that it is possible to use simple features to achieve an artifact detection sensitivity and specificity higher than 90%.

Can PPG be used for HRV analysis?

Heart rate variability (HRV) represents one of the most promising markers of the autonomic nervous system (ANS) regulation. However, it requires the acquisition of the ECG signal in order to reliably detect the RR intervals, which is not always easily and comfortably available in personal health applications. Additionally, due to progress in single spot optical sensors, photoplethysmography (PPG) is an interesting alternative for heartbeat interval measurements, since it is a more convenient and a less intrusive measurement technique. Driven by the technological advances in such sensors, wrist-worn devices are becoming a commodity, and the interest in the assessment of HRV indexes from the PPG analysis (pulse rate variability - PRV) is rising. In this study, we investigate the hypothesis of using PRV features as surrogates for HRV indexes, in three different contexts: healthy subjects at rest, healthy subjects after physical exercise and subjects with cardiovascular diseases (CVD). Additionally, we also evaluate which are the characteristic points better suited for PRV analysis in these contexts, i.e. the PPG waveform characteristic points leading to the PRV features that present the best estimates of HRV (correlation and error analysis). The achieved results suggest that the PRV can be often used as an alternative for HRV analysis in healthy subjects, with significant correlations above 82%, for both time and frequency features. Contrarily, in the post-exercise and CVD subjects, time and (most importantly) frequency domain features shall be used with caution (mean correlations ranging from 68% to 88%).

Diabetic foot ulcer development risk classifications' validation: A multicentre prospective cohort study

AIMS

To prospectively validate the existing classifications to stratify subjects with diabetes mellitus (DM) by their risk of diabetic foot ulcer (DFU), in high and low risk settings.

METHODS

A prospective multicentre cohort study was conducted, including 446 subjects with DM without active DFU followed in the hospital or primary care setting. Demographic, clinical characterization variables, and those included in the classifications were collected at baseline. Subjects were followed for 1year, until DFU or death.

RESULTS

In our sample, with a mean age of 65years, 52% were male; 32 developed a DFU, 7 required an amputation and 18 died. Differences were found between participants' characteristics and classifications' accuracy according to the setting. The great majority of the variables were associated with higher DFU risk. Globally, classifications were highly and equally valid, positive predictive values (PV) were inferior to 40%, negative PV superior to 90% and area under the receiver operating characteristic curve superior to 0.75.

DISCUSSION

All the existing classifications are valid to be applied in high risk clinical context and have a very high capacity to categorize as low risk those subjects that will not develop a DFU. Further research is needed in the primary care setting.

Detection of different types of noise in lung sounds

Lung sound signal processing has proven to be a great improvement to the traditional acoustic interpretation of lung sounds. However, that analysis can be seriously hindered by the presence of different types of noise originated in the acquisition environment or caused by physiological processes. Consequently, the diagnostic accuracy of pulmonary diseases can be severely affected, especially if the implementation of telemonitoring systems is considered. The present study is focused on the implementation of an algorithm able to identify noisy periods, either voluntarily (vocalizations, chest movement and background voices) or involuntarily produced during acquisitions of lung sounds. The developed approach also had to deal with the presence of simulated cough events, that carry important diagnostic information regarding several pulmonary diseases. Features such as Katz fractal dimension, Teager-Kaiser energy operator and normalized mutual information, were extracted from the time domain of healthy and a pathological lung signals. Noise detection was the result of a good discrimination between uncontaminated lung sounds and both cough and noise episodes and a slightly worse classification of cough events. In fact, detection of cough periods carrying diagnostic information was influenced by the presence of two other types of noise having similar signal characteristics.

In vitro and in vivo evaluation of a new zirconia/niobium biocermet for hard tissue replacement

Metals and ceramics are commonly used in orthopaedics, dentistry and other load bearing applications. However, the use of ceramic matrix composites reinforced with biocompatible metals for heavy load-bearing hard tissue replacement applications has not previously been reported. In order to improve the reliability and the mechanical properties of biomedical implants, new zirconia-Nb composites have been recently developed. The aim of this study was to investigate the biological tolerance of these new zirconia/Nb biocermets implants with both in vitro and in vivo approaches. At first, human bone marrow derived mesenchymal stem cells were cultured on sintered biocermet discs with polished surfaces and were compared with responses to niobium metal. In vitro, the biocermets showed no deleterious effect on cell proliferation, extra-cellular matrix production or on cell morphology. Furthermore, the biocermet showed a higher percentage of cell proliferation than Nb metal. On the other hand, the bone response to these new zirconia/Nb biocermets was studied. Cylinders of biocermets, as well as commercially Nb rod were implanted in the tibiae of New Zealand white rabbits. All the animals were euthanatized after 6 months. The specimens were processed to obtain thin ground sections. The slides were observed in normal transmitted light microscope. A newly formed bone was observed in close contact with material surfaces. No inflamed or multinucleated cells were present. This study concluded that zirconia/Nb composites are biocompatible and osteoconductive. The ceramic-metal composite has even better osteointegration ability than pure Nb. In conclusion, zirconia-Nb biocermet is suitable for heavy load-bearing hard tissue replacement from the point of view of both mechanical properties and biocompatibility.

A novel multi-parametric algorithm for faint prediction integrating indices of cardiac inotropy and vascular tone

Neurally medicated syncope (NMS) patients suffer from sudden loss of consciousness, which is associated with a high rate of falls and hospitalization. NMS negatively impacts a subject's quality of life and is a growing cost issue for the healthcare systems in particular since mainly elderly are at risk of NMS in our aging societies. In the present paper we present an algorithm for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Several parameters extracted from ECG and PPG, which have been associated in previous works with reflectory mechanisms underlying NMS, were combined in a single algorithm to detect impending syncope. The proposed algorithm was validated in 43 subjects using a 3-way data split scheme and achieved the following performance: sensitivity (SE) - 100%; specificity (SP) - 92%; positive predictive value (PPV) - 85%; false positive rate per hour (FPRh) - 0.146h(-1) and; average prediction time (aPTime) - 217.58s.

Real-Time Prediction of Neurally Mediated Syncope

Neurally mediated syncope (NMS) patients suffer from sudden loss of consciousness, which is associated with a high rate of falls and hospitalization. NMS negatively impacts a subject's quality of life and is a growing cost issue in our aging society, as its incidence increases with age. In this paper, we present a solution for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) alone. Several parameters extracted from ECG and PPG, associated with reflectory mechanisms underlying NMS in previous publications, were combined in a single algorithm to detect impending syncope. The proposed algorithm was evaluated in a population of 43 subjects. The feature selection, distance metric selection, and optimal threshold were performed in a subset of 30 patients, while the remaining data from 13 patients were used to test the final solution. Additionally, a leave-one-out cross-validation scheme was also used to evaluate the performance of the proposed algorithm yielding the following results: sensitivity (SE)--95.2%; specificity (SP)--95.4%; positive predictive value (PPV)--90.9%; false-positive rate per hour (FPRh)-0.14 h(-1), and prediction time (aPTime)--116.4 s.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Detection of motion artifacts in photoplethysmographic signals based on time and period domain analysis

The presence of motion artifacts in the photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in real time and continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection, which is based on the analysis of the variations in the time and period domain characteristics of the PPG signal. The extracted features are ranked using a feature selection algorithm (NMIFS) and the best features are used in a Support Vector Machine classification model to distinguish between clean and corrupted sections of the PPG signal. The results achieved by the current algorithm (SE: 0.827 and SP: 0.927) show that both time and especially period domain features play an important role in the discrimination of motion artifacts from clean PPG pulses.

Multi-Gaussian fitting for the assessment of left ventricular ejection time from the photoplethysmogram

The Left ventricular ejection time (LVET) is one of the primary surrogates of the left ventricular contractility and stroke volume. Its continuous monitoring is considered to be a valuable hypovolumia prognostic parameter and an important risk predictor in cardiovascular diseases such as cardiac and light chain amyloidosis. In this paper, we present a novel methodology for the assessment of LVET based the Photoplethysmographic (PPG) waveform. We propose the use of Gaussian functions to model both systolic and diastolic phases of the PPG beat and consequently determine the onset and offset of the systolic ejection from the analysis of the systolic phase 3(rd) derivative. The results achieved by the proposed methodology were compared with the algorithm proposed by Chan et al. [1], revealing better estimation of LVET (15.84 ± 13.56 ms vs 23.01 ± 14.60 ms), and similar correlation with the echocardiographic reference (0.73 vs 0.75).

Beat-to-beat cardiac output inference using heart sounds

Cardiac output (CO) change is the primary compensatory mechanism that responds to oxygenation demand. Its continuous monitoring has great potential for the diagnosis and management of cardiovascular diseases, both in hospital as well as in ambulatory settings. However, CO measurements are currently limited to hospital settings only. In this paper, we present an extension of the model proposed by Finkelstein for beat-to-beat CO assessment. We use a nonlinear model consisting of a two-layer feed-forward artificial neural network. In addition to demographic (body surface area and age) and physiological parameters (HR), surrogates of contractility, afterload and mean arterial pressure based on systolic time intervals (STIs), estimated from echocardiography and heart sounds are used as inputs to our models. The results showed that the proposed models--with echocardiography as reference--produce better estimations of stroke volume/CO than the Finkelstein model (12.83 ± 10.66 ml vs 7.23 ± 6.6 ml), as well as higher correlation (0.46 vs 0.82).

Comparison of systolic time interval measurement modalities for portable devices

Systolic time intervals (STI) have shown significant diagnostic and prognostic value to assess the global cardiac function. Their value has been largely established in hospital settings. Currently, STI are considered a promising tool for long-term patient follow-up with chronic cardiovascular diseases. Several technologies exist that enable beat-by-beat assessment of STI in personal health application scenarios. A comparative study is presented using the echocardiographic gold standard synchronized with impedance cardiography (ICG), phonocardiography (PCG) and photoplethysmography (PPG). The ability of these competing technologies in assessing the pre ejection period (PEP) and the left ventricle ejection time (LVET) is given a general overview with comparative results.

Assessing systolic time-intervals from heart sound: a feasibility study

Systolic time intervals are highly correlated to fundamental cardiac functions. In this paper we investigate the feasibility of using heart sound (HS) to accurately measure the opening and closing moments of the aortic valve, since these are crucial moments to define the main systolic timings of the heart cycle, i.e. the pre-ejection period (PEP) and the left ventricular ejection time (LVET). We introduce a HS model, which is applied to define several features that provide clear markers to identify these moments in the HS. Using these features and a comparative analysis with registered echocardiographies from 17 subjects, the results achieved in this study suggest that HS can be used to accurately estimate LVET and PEP.

On the detection of Premature Ventricular Contractions

Premature Ventricular Contractions (PVC) are a cardiac arrhythmia that can be associated with an increased risk of adverse cardiac events such as ventricular arrhythmias and sudden death. Therefore, the characterization of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. In this paper a new approach for automatic detection of PVCs is presented, based on morphological derivatives and information theory techniques. Using these approaches a set of patient invariant features is introduced. Sensibility and specificity results (respectively, 96.35% and 99.15%) show the potential of the algorithm when applied to the MIT-BIH Arrhythmia database.