102 research outputs found
The Informativeness of Comprehensive Income and Its Components for Chinese Listed Banks: Important Implications for Accounting Standards-Setting and Banking Supervision
最近与公允价值会计相关的会计准则制定和银行监管活动为公允价值法的信息含量和决策有用性问题注入了新的活力,并使得公允价值对财务稳定性是否会产生意想不到的效果这一问题浮出水面。此外,越来越多的银行将某些公允价值计量的资产或负债的公允价值变动计入到其他综合收益(OCI)当中,列示在报表的净利润项目之下。IASB财务准则概念框架准则(2013,8.46)制定目标认为,OCI能够增强利得和损失的决策相关性,本文受之启发,加上中国经济的下滑以及银行高不良贷款率的问题,增加了市场风险。2009年,财政部颁布了企业会计准则(No.3)-其他综合收益列报,要求所有上市公司在利润表中,在“每股收益”项下增加披露其...Recent Accounting Standard Setting and Bank Supervision activity related to fair value accounting (FVA) has injected new life into questions of whether a fair value approach provides an informative and signals to decision-making, and whether there might be unintended consequences on financial stability. In addition, an increasing number of banks measured certain financial instruments on assets and...学位:管理学博士院系专业:管理学院_会计学学号:1752013015438
Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach
Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal
breathing. The severity of OSA can lead to many symptoms such as sudden cardiac
death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It
records many signals from the patient's body for at least one whole night and
calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or
hypopnea incidences per hour. This value is then used to classify patients into
OSA severity levels. However, it has many disadvantages and limitations.
Consequently, we proposed a novel methodology of OSA severity classification
using a Deep Learning approach. We focused on the classification between normal
subjects (AHI 30). The 15-second raw
ECG records with apnea or hypopnea events were used with a series of deep
learning models. The main advantages of our proposed method include easier data
acquisition, instantaneous OSA severity detection, and effective feature
extraction without domain knowledge from expertise. To evaluate our proposed
method, 545 subjects of which 364 were normal and 181 were severe OSA patients
obtained from the MrOS sleep study (Visit 1) database were used with the k-fold
cross-validation technique. The accuracy of 79.45\% for OSA severity
classification with sensitivity, specificity, and F-score was achieved. This is
significantly higher than the results from the SVM classifier with RR Intervals
and ECG derived respiration (EDR) signal feature extraction. The promising
result shows that this proposed method is a good start for the detection of OSA
severity from a single channel ECG which can be obtained from wearable devices
at home and can also be applied to near real-time alerting systems such as
before SCD occurs
Combining EEG and NLP Features for Predicting Students' Lecture Comprehension using Ensemble Classification
Electroencephalography (EEG) and Natural Language Processing (NLP) can be
applied for education to measure students' comprehension in classroom lectures;
currently, the two measures have been used separately. In this work, we propose
a classification framework for predicting students' lecture comprehension in
two tasks: (i) students' confusion after listening to the simulated lecture and
(ii) the correctness of students' responses to the post-lecture assessment. The
proposed framework includes EEG and NLP feature extraction, processing, and
classification. EEG and NLP features are extracted to construct integrated
features obtained from recorded EEG signals and sentence-level syntactic
analysis, which provide information about specific biomarkers and sentence
structures. An ensemble stacking classification method -- a combination of
multiple individual models that produces an enhanced predictive model -- is
studied to learn from the features to make predictions accurately. Furthermore,
we also utilized subjective confusion ratings as another integrated feature to
enhance classification performance. By doing so, experiment results show that
this framework performs better than the baselines, which achieved F1 up to 0.65
for predicting confusion and 0.78 for predicting correctness, highlighting that
utilizing this has helped improve the classification performance
Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification
Dramatic raising of Deep Learning (DL) approach and its capability in
biomedical applications lead us to explore the advantages of using DL for sleep
Apnea-Hypopnea severity classification. To reduce the complexity of clinical
diagnosis using Polysomnography (PSG), which is multiple sensing platform, we
incorporates our proposed DL scheme into one single Airflow (AF) sensing signal
(subset of PSG). Seventeen features have been extracted from AF and then fed
into Deep Neural Networks to classify in two studies. First, we proposed a
binary classifications which use the cutoff indices at AHI = 5, 15 and 30
events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity
classification was proposed to classify patients into 4 groups including no
SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we
used a higher number of patients than related works to accommodate more
diversity which includes 520 AF records obtained from the MrOS sleep study
(Visit 2) database. We then applied the 10-fold cross-validation technique to
get the accuracy, sensitivity and specificity. Moreover, we compared the
results from our main classifier with other two approaches which were used in
previous researches including the Support Vector Machine (SVM) and the
Adaboost-Classification and Regression Trees (AB-CART). From the binary
classification, our proposed method provides significantly higher performance
than other two approaches with the accuracy of 83.46 %, 85.39 % and 92.69 % in
each cutoff, respectively. For the multiclass classification, it also returns a
highest accuracy of all approaches with 63.70 %
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