Medical Signals & Sensors

Computationally efficient system matrix calculation techniques in computed tomography iterative reconstruction Golshan Mahmoudi, Mohammad Reza Ay, Arman Rahmim, Hossein Ghadiri Journal of Medical Signals & Sensors 2020 10(1):1-11 Background: Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line–area integral model (AIM) that highlights the advantages of both LIM and AIMs. Methods: The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM). Results: The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0.05), though computations were 4.9 times more intensive than the proposed LIM. Conclusion: We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM–AIM that achieves significantly improved images though with its computational requirements.

A Semi-supervised method for tumor segmentation in mammogram images Hanie Azary, Monireh Abdoos Journal of Medical Signals & Sensors 2020 10(1):12-18 Background: Breast cancer is one of the most common cancers in women. Mammogram images have an important role in the treatment of various states of this cancer. In recent years, machine learning methods have been widely used for tumor segmentation in mammogram images. Pixel-based segmentation methods have been presented using both supervised and unsupervised learning approaches. Supervised learning methods are usually fast and accurate, but they usually use a large number of labeled data. Besides, providing these samples is very hard and usually expensive. Unsupervised learning methods do not require the labels of the training data for decision making and they completely ignore the prior knowledge that may lead to a low performance. Semi-supervised learning methods which use a small number of labeled data solve the problem of providing the high number of samples in supervised methods, while they usually result in a higher accuracy in comparison to the unsupervised methods. Methods: In this study, we used a semisupervised method for tumor segmentation in which the pixel information is used for the classification. The static and gray level run length matrix features for each pixel are considered as the features, and Fisher discriminant analysis (FDA) is used for feature reduction. A cotraining algorithm based on support vector machine and Bayes classifiers is proposed for tumor segmentation on MIAS data set. Results and Conclusion: The results show that the proposed method outperforms both supervised methods.

Wearable wireless sensors for measuring calorie consumption Faranak Fotouhi-Ghazvini, Saedeh Abbaspour Journal of Medical Signals & Sensors 2020 10(1):19-34 Background: The tracking devices could help measuring the heart rate and energy expenditure and recognizing the user's activity. The calorie measurement is a significant achievement for the fitness tracking and the continuous health monitoring. Methods: In this paper, a combination of an accelerometer and a photoplethysmography (PPG) sensor is implemented to calculate the calories consumed. These sensors were mounted next to each other and then were placed on the ankle and finger by flat cable. The sensed data are transferred via Bluetooth to a smartphone in a serial and real-time manner. An Android App is designed to display the user's health data. The average amount of consumed energy is obtained from the combination of the accelerometer sensor based on the laws of motion and the PPG sensor based on the heart rate data. Results: The designed system is tested on 10 nonathlete males and 10 nonathlete females randomly. By applying thevelet, the value of the acceleration signal variance was reduced from 3.2 to 0.8. The correlation between PPG and pulse oximeter was 0.9. Moreover, the correlation of the accelerometer and treadmill was 0.9. The root mean square error (RMSE) and the P value of the calorie output from PPG and pulse oximeter are 0.53 and 0.008, respectively. The RMSE and the P value of the calories output from the accelerometer and the treadmill are 0.42 and 0.007, respectively. Conclusion: Our device validity and reliability were good by comparing it with a typical smart band, smart watch, and smartphone available in the market. The combined PPG and the accelerometer sensors were compared with the gold standard, the pulse oximeter, and the treadmill. According to the results, there is no significant difference in the values obtained. Therefore, a mobile system is augmented with the wireless accelerometer and PPG that are connected to a smartphone. The system could be carried out with the user at any time and any place.

A novel non-enzymatic biosensor based on Ti-metallic glass thin film: The blood glucose oxidation approach Mohsen Sarafbidabad, Hamidreza Kaviani Jazi, Mohammad Rafienia Journal of Medical Signals & Sensors 2020 10(1):35-41 Background: Material selection is a key issue for the fabrication of non-enzymatic electrode in glucose biosensors. Metallic glass (MG) as an advanced innovative material can provides many basic structural requirements of electrodes. A novel non-enzymatic biosensor based on Ti57Cu28{Zr0.95−Hf0.05}XSi15-XMG (Ti-MG) thin film was introduced for glucose oxidation. Methods: The Ti-MG thin film was deposited on the carbon substrate of screen-printed carbon electrode (SPCE), and the Ti-MG modified SPCE was fabricated as Ti-MG/SPCE. The morphology and structure of the Ti-MG thin film were characterized by field emission scanning electron microscope and X-ray diffraction. Electrochemical evaluations were studied by electrochemical impedance spectroscopy and cyclic voltammetry. Results: The Ti-MG was sputtered on the carbon substrate in the form of a porous spongy thin film with 285 nm thickness and nanoparticles with average diameter size of 110 nm. The Ti-MG/SPCE showed low charge transfer resistance to the electron transfer and high electrocatalytic activity toward the oxidation of glucose in PBS (pH = 7.4) solution. This biosensor exhibited good analytical performance with a linear range from 2 to 8 mM glucose and sensitivity of 0.017 μA mM−1. Conclusion: The experimental results indicate that Ti-MG thin film has a high ability to electron transfer and glucose oxidation for the development of non-enzymatic glucose biosensors.

The measurement of thyroid absorbed dose by gafchromic™ EBT2 film and changes in thyroid hormone levels following radiotherapy in patients with breast cancer Leyla Ansari, Neda Nasiri, Fahimeh Aminolroayaei, Karim Ghazikhanlou Sani, Masoumeh Dorri-Giv, Razzagh Abedi-Firouzjah, Dariush Sardari Journal of Medical Signals & Sensors 2020 10(1):42-47 Background: Radiotherapy is a main method for the treatment of breast cancer. This study aimed to measure the absorbed dose of thyroid gland using Gafchromic EBT2 film during breast cancer radiotherapy. In addition, the relationship between the absorbed dose and thyroid hormone levels was evaluated. Methods: Forty-six breast cancer patients, with the age ranged between 25 and 35 years, undergoing external radiotherapy were studied. The patients were treated with 6 and 18 MV X-ray beams, and the absorbed thyroid dose was measured by EBT2 film. Thyroid hormone levels, thyroid-stimulating hormone (TSH), triiodothyronine (T3), and thyroxin (T4), were measured before and after the radiotherapy. Pearson's, Spearman's, and Chi-square tests were performed to evaluate the correlation between the thyroid dose and hormone levels. Results: The mean thyroid dose was 26 ± 9.45 cGy with the range of 7.85–48.35 cGy. There were not any significant differences at thyroid hormone levels between preradiotherapy and postradiotherapy (P > 0.05). There was a significant relationship between increased thyroid absorbed dose and changes in TSH and T4 levels (P < 0.05), but it was not significant in T3 level (P = 0.1). Conclusion: Regarding the results, the thyroid absorbed dose can have an effect on its function. Therefore, the thyroid gland should be considered as an organ at risk in breast cancer radiotherapy.

Evaluation of the lung dose in three-dimensional conformal radiation therapy of left-sided breast cancer: A phantom study Mahsa Abdemanafi, Mohammad Bagher Tavakoli, Ali Akhavan, Iraj Abedi Journal of Medical Signals & Sensors 2020 10(1):48-52 Background: Three-dimensional 3D-CRT: conformal radiation therapy is a selective modality in many radiotherapy centers for the treatment of breast cancer. One of the most common side effects of this method is radiation lung injury. Considering such an injury, lung dose deserves to be studied in depth. Methods: Computed tomography scan of a node-positive left-sided breast cancer woman was used for generating a thorax phantom. Ten thermoluminescent dosimeters (TLDs) were distributed evenly in the left lung of the phantom, and the phantom was scanned. The optimal plan, including supraclavicular and tangential fields, was created by the treatment planning system (TPS). The results of TLD dose measurements at the selected points in the phantom were compared to TPS dose calculations. Results: Lung doses calculated by TPS are significantly different from those measured by the TLDs (P = 0.007). The minimum and maximum differences were −0.91% and 4.46%, respectively. TLDs that were on the inner margin of the lung and breast tissue showed higher dose differences than the TLDs in the lung. Conclusion: The results of this study showed that TPS generally overestimated doses compared to TLD measurements due to incorrect beam modeling caused by contaminated electrons in the lung.

Chaos-based analysis of heart rate variability time series in obstructive sleep apnea subjects Shiva Naghsh, Mohammad Ataei, Mohammadreza Yazdchi, Mohammad Hashemi Journal of Medical Signals & Sensors 2020 10(1):53-59 Obstructive sleep apnea (OSA) is a common disorder which can cause periodic fluctuations in heart rate. To diagnose sleep apnea, some studies analyze electrocardiogram (ECG) signals by adopting chaos-based analysis. This research is going to specifically focus on whether it is possible to use chaos-based analysis of heart rate variability (HRV) signals rather than using chaotic analysis of ECG signals to diagnose OSA. While conventional studies mostly use chaos-based analysis of ECG signals to detect OSA, here, we apply correlation dimension (CD) as a chaotic index to analyze HRV data in OSA patients. For this purpose, 17 patients with OSA and 9 healthy individuals referred to a sleep clinic in Isfahan/Iran are studied, and their HRV time series were extracted from 1-h ECG signals recorded overnight. The preliminary step to calculate CD is phase-space reconstruction of the system based on HRV time series. Corresponding parameters, including embedding dimension and lag time, are estimated optimally using enhanced related methods, and then CD is calculated using Grassberger–Procaccia algorithm. Moreover, to evaluate our results, detrended fluctuation analysis (DFA), one of the well-known nonlinear methods in HRV analysis to detect OSA, is also applied to our data and the result is compared with those obtained from CD analysis of HRV. CD index with P < 0.005 indicates a significant difference in nonlinear dynamics of HRV signals detected from OSA patients and healthy individuals.

A hybrid method for the diagnosis and classifying parkinson's patients based on time–frequency domain properties and K-nearest neighbor Zayrit Soumaya, Belhoussine Drissi Taoufiq, Nsiri Benayad, Benba Achraf, Abdelkrim Ammoumou Journal of Medical Signals & Sensors 2020 10(1):60-66 The vibrations of hands and arms are the main symptoms of Parkinson's ailment. Nevertheless, the affection of the vocal cords leads to troubles and defects in the speech, which is another accurate symptom of the disease. This article presents a diagnostic model of Parkinson's disease (PD) and proposes the time–frequency transform (wavelet WT) and Mel-frequency cepstral coefficients (MFCC) treatment for this disease. The proposed treatment is centered on the vocal signal transformation by a method based on the WT and to extract the coefficients of the MFCC and eventually the categorization of the sick and healthy patients by the use of the classifier K-nearest neighbor (KNN). The analysis used in this article uses a database that contains 18 healthy patients and twenty patients. The Daubechies mother WT is used in treatments to compress the vocal signal and extract the MFCC cepstral coefficients. As far as, the diagnosis of Parkinson's ailment is concerned the KNN classifying performance gives 89% accuracy when applied to 52% of the database as training data, whereas when we increase this percentage from 52% to 73%, we reach 98.68% accuracy which is higher than using the support-vector machine classifier. The KNN is conclusive in the determination of the PD. Moreover, the higher the training data is, the more precise the results are.