Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers

IntroductionIn the clinical setting, it becomes increasingly important to detect epileptic seizures automatically since it could significantly reduce the burden for the care of patients suffering from intractable epilepsy. Electroencephalography (EEG) signals record the brain's electrical activity and contain rich information about brain dysfunction. As a non-invasive and inexpensive tool for detecting epileptic seizures, visual evaluation of EEG recordings is labor-intensive and subjective and requires significant improvement.MethodsThis study aims to develop a new approach to recognize seizures automatically using EEG recordings. During feature extraction of EEG input from raw data, we construct a new deep neural network (DNN) model. Deep feature maps derived from layers placed hierarchically in a convolution neural network are put into different kinds of shallow classifiers to detect the anomaly. Feature maps are reduced in dimensionality using Principal Component Analysis (PCA).ResultsBy analyzing the EEG Epilepsy dataset and the Bonn dataset for epilepsy, we conclude that our proposed method is both effective and robust. These datasets vary significantly in the acquisition of data, the formulation of clinical protocols, and the storage of digital information, making processing and analysis challenging. On both datasets, extensive experiments are performed using a cross-validation by 10 folds strategy to demonstrate approximately 100% accuracy for binary and multi-category classification.DiscussionIn addition to demonstrating that our methodology outperforms other up-to-date approaches, the results of this study also suggest that it can be applied in clinical practice as well

Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients

DNA methylation changes during aging, but it remains unclear whether the effect of DNA methylation on lung cancer survival varies with age. Such an effect could decrease prediction accuracy and treatment efficacy. We performed a methylation-age interaction analysis using 1,230 early-stage lung adenocarcinoma patients from five cohorts. A Cox proportional hazards model was used to investigate lung adenocarcinoma and squamous cell carcinoma patients for methylation-age interactions, which were further confirmed in a validation phase. We identified one adenocarcinoma-specific CpG probe, cg14326354, with effects significantly modified by age (HR = 0.989; 95% CI: 0.986-0.994; P = 9.18×10-7). The effect of low methylation was reversed for young and elderly patients categorized by the boundary of 95% CI standard (HR = 2.44; 95% CI: 1.26-4.72; P = 8.34×10-3; HR = 0.58; 95% CI: 0.42-0.82; P = 1.67×10-3). Moreover, there was an antagonistic interaction between low cg14326354PRODH methylation and elderly age (HR = 0.21; 95% CI: 0.11-0.40; P = 2.20×10-6). In summary, low methylation of cg14326354 might benefit survival of elderly lung adenocarcinoma patients, providing new insight to age-specific prediction and potential drug targeting

Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients

DNA methylation changes during aging, but it remains unclear whether the effect of DNA methylation on lung cancer survival varies with age. Such an effect could decrease prediction accuracy and treatment efficacy. We performed a methylation-age interaction analysis using 1,230 early-stage lung adenocarcinoma patients from five cohorts. A Cox proportional hazards model was used to investigate lung adenocarcinoma and squamous cell carcinoma patients for methylation-age interactions, which were further confirmed in a validation phase. We identified one adenocarcinoma-specific CpG probe, cg14326354PRODH, with effects significantly modified by age (HRinteraction = 0.989; 95% CI: 0.986-0.994; P = 9.18×10-7). The effect of low methylation was reversed for young and elderly patients categorized by the boundary of 95% CI standard (HRyoung = 2.44; 95% CI: 1.26-4.72; P = 8.34×10-3; HRelderly = 0.58; 95% CI: 0.42-0.82; P = 1.67×10-3). Moreover, there was an antagonistic interaction between low cg14326354PRODH methylation and elderly age (HRinteraction = 0.21; 95% CI: 0.11-0.40; P = 2.20×10-6). In summary, low methylation of cg14326354PRODH might benefit survival of elderly lung adenocarcinoma patients, providing new insight to age-specific prediction and potential drug targeting

SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis

Smoking cessation prolongs survival and decreases mortality of patients with non‐small‐cell lung cancer (NSCLC). In addition, epigenetic alterations of some genes are associated with survival. However, potential interactions between smoking cessation and epigenetics have not been assessed. Here, we conducted an epigenome‐wide interaction analysis between DNA methylation and smoking cessation on NSCLC survival. We used a two‐stage study design to identify DNA methylation-smoking cessation interactions that affect overall survival for early‐stage NSCLC. The discovery phase contained NSCLC patients from Harvard, Spain, Norway, and Sweden. A histology‐stratified Cox proportional hazards model adjusted for age, sex, clinical stage, and study center was used to test DNA methylation-smoking cessation interaction terms. Interactions with false discovery rate‐q ≤ 0.05 were further confirmed in a validation phase using The Cancer Genome Atlas database. Histology‐specific interactions were identified by stratification analysis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. We identified one CpG probe (cg02268510SIPA1L3) that significantly and exclusively modified the effect of smoking cessation on survival in LUAD patients [hazard ratio (HR)interaction = 1.12; 95% confidence interval (CI): 1.07-1.16; P = 4.30 × 10-7]. Further, the effect of smoking cessation on early‐stage LUAD survival varied across patients with different methylation levels of cg02268510SIPA1L3. Smoking cessation only benefited LUAD patients with low methylation (HR = 0.53; 95% CI: 0.34-0.82; P = 4.61 × 10-3) rather than medium or high methylation (HR = 1.21; 95% CI: 0.86-1.70; P = 0.266) of cg02268510SIPA1L3. Moreover, there was an antagonistic interaction between elevated methylation of cg02268510SIPA1L3 and smoking cessation (HRinteraction = 2.1835; 95% CI: 1.27-3.74; P = 4.46 × 10−3). In summary, smoking cessation benefited survival of LUAD patients with low methylation at cg02268510SIPA1L3. The results have implications for not only smoking cessation after diagnosis, but also possible methylation‐specific drug targeting

Epigenetic-smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early‐stage NSCLC patients

Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non-small-cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early-stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407TRIM27 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30-2.09, P = 4.52 × 10-5), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87-1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407TRIM27 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49LUAD vs 54.79LUSC, P = 1.03 × 10-19) and included a higher proportion of current smokers than LUAD patients (28.24%LUAD vs 34.09%LUSC, P = 0.037). cg05293407TRIM27 was significantly associated with overall survival only in NSCLC patients with medium-high pack-year of smoking (HR = 1.58, 95% CI: 1.26-1.96, P = 5.25 × 10-5). We conclude that cg05293407TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC

Epigenetic-smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early-stage NSCLC patients

Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non-small-cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early-stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30-2.09, P = 4.52 × 10), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87-1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49 vs 54.79, P = 1.03 × 10) and included a higher proportion of current smokers than LUAD patients (28.24% vs 34.09%, P = 0.037). cg05293407 was significantly associated with overall survival only in NSCLC patients with medium-high pack-year of smoking (HR = 1.58, 95% CI: 1.26-1.96, P = 5.25 × 10). We conclude that cg05293407 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC

Biotransformation and detoxification of inorganic arsenic in a marine juvenile fish Terapon jarbua after waterborne and dietborne exposure

Arsenic (As) is a major hazardous metalloid in many aquatic environments. This study quantified the biotransformation of two inorganic As species [As(III) and As(V)] in a marine juvenile grunt Terapon jarbua following waterborne and dietborne exposures for 10 d. The fish were fed As contaminated artificial diets at nominal concentrations of 50,150, and 500 As(III) and As(V)/g (dry weight), and their transformation and growth responses were compared to those exposed to 100 mu g/L waterborne As(III) and As(V). Within the 10 d exposure period, waterborne and dietborne inorganic As exposure had no significant effect on the fish growth performance. The bioaccumulation of As was very low and not proportional to the inorganic As exposure concentration. We demonstrated that both inorganic As(III) and As(V) in the dietborne and waterborne phases were rapidly biotransformed to the less toxic arsenobetaine (AsB, 89-97%). After exposure to inorganic As. T. jarbua developed correspondingly detoxified strategies, such as the reduction of As(V) to As(III) followed by methylation to less toxic organic forms, as well as the synthesis of metal-binding proteins such as metallothionein-like proteins. This study elucidated that As(III) and As(V) had little potential toxicity on marine fish. (C) 2012 Elsevier B.V. All rights reserved

Perturbation analysis and condition numbers of scaled total least squares problems

The standard approaches to solving an overdetermined linear system Ax∈≈∈b find minimal corrections to the vector b and/or the matrix A such that the corrected system is consistent, such as the least squares (LS), the data least squares (DLS) and the total least squares (TLS). The scaled total least squares (STLS) method unifies the LS, DLS and TLS methods. The classical normwise condition numbers for the LS problem have been widely studied. However, there are no such similar results for the TLS and the STLS problems. In this paper, we first present a perturbation analysis of the STLS problem, which is a generalization of the TLS problem, and give a normwise condition number for the STLS problem. Different from normwise condition numbers, which measure the sizes of both input perturbations and output errors using some norms, componentwise condition numbers take into account the relation of each data component, and possible data sparsity. Then in this paper we give explicit expressions for the estimates of the mixed and componentwise condition numbers for the STLS problem. Since the TLS problem is a special case of the STLS problem, the condition numbers for the TLS problem follow immediately from our STLS results. All the discussions in this paper are under the Golub-Van Loan condition for the existence and uniqueness of the STLS solution.</p

Condition Numbers for the Scaled Total Least Squares Problems ∗

The standard approaches to solving an overdetermined linear system Bx ≈ c construct minimal corrections to the vector c and/or the matrix B such that the corrected system is consistent, such as the least squares (LS), the data least squares (DLS) and the total least squares (TLS). The scaled total least squares (STLS) method unifies the LS, DLS and TLS methods. So far, the classical normwise condition numbers for the LS problem have been widely studied. However, there are no such similar results for the TLS and the STLS problems. In this paper we first present a normwise condition number for the STLS problem. Different from normwise condition numbers which measure the sizes of both input perturbations and output errors using some norms, componentwise condition numbers take into account the relation of each data component, and a possible data sparsity. Then in this paper we give explicit expressions for mixed and componentwise condition numbers for the STLS problem. Moreover, as a byproduct, we also derive normwise and componentwise condition numbers for the difference between the STLS solution and the LS solution. Since the TLS problem is a special case of the STLS problem, the condition numbers for the TLS problem follow immediately from our STLS results