<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel rdf:about="http://localhost:8080/xmlui/handle/123456789/3433">
    <title>DSpace Collection:</title>
    <link>http://localhost:8080/xmlui/handle/123456789/3433</link>
    <description />
    <items>
      <rdf:Seq>
        <rdf:li rdf:resource="http://localhost:8080/xmlui/handle/123456789/3434" />
      </rdf:Seq>
    </items>
    <dc:date>2026-06-23T06:29:55Z</dc:date>
  </channel>
  <item rdf:about="http://localhost:8080/xmlui/handle/123456789/3434">
    <title>A Novel Hybrid Framework for Cuff-Less Blood Pressure Estimation based On Vital Bio Signals processing using Machine Learning</title>
    <link>http://localhost:8080/xmlui/handle/123456789/3434</link>
    <description>Title: A Novel Hybrid Framework for Cuff-Less Blood Pressure Estimation based On Vital Bio Signals processing using Machine Learning
Authors: Shinde, Santosh A; Rajeswari, P. Raja
Abstract: Blood Pressure is one among the most important &#xD;
physiological parameters for assessing the overall well being &#xD;
of an individual. It plays pivotal role in the detection of many &#xD;
cardiovascular diseases specially Hypertension. Traditional &#xD;
Cuff-Based BP measurements techniques have several &#xD;
drawbacks and they are significantly inconvenient to patients, &#xD;
moreover continuous BP measurement is difficult. Lot of &#xD;
research is currently going on for Cuff-Less BP Estimation &#xD;
and several techniques are researched out in the researcher’s &#xD;
community. However, most of the existing approaches lack &#xD;
the required level of accuracy, generality and they are not &#xD;
experimented out on a large population of having &#xD;
heterogeneous subjects with varied demographic features. In &#xD;
this paper we propose a novel hybrid signal processing &#xD;
approach using machine learning for continuous estimation &#xD;
of BP without the need for calibration. Our proposed &#xD;
framework has reached satisfactory results in terms of Mean &#xD;
Absolute Error (MAE) for mean arterial pressure (MAP)&#xD;
Estimation.</description>
    <dc:date>2020-04-10T00:00:00Z</dc:date>
  </item>
</rdf:RDF>

