diff --git a/CHANGELOG.md b/CHANGELOG.md
index f75a6837..933d78ae 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@
 ## Other changes
 - Update setup.py & requirements.txt & requirements-dev.txt - [#1316](https://github.com/jertel/elastalert2/pull/1316) - @nsano-rururu
 - [Docs] Clarify how to reference query_key values in flatline alerts - [#1320](https://github.com/jertel/elastalert2/pull/1320) - @jertel
+- Fix percentiles aggregation type in Spike Metric Aggregation rules - [#1323](https://github.com/jertel/elastalert2/pull/1323) - @jertel
 
 # 2.15.0
 
diff --git a/elastalert/ruletypes.py b/elastalert/ruletypes.py
index 9200f2c4..47509f62 100644
--- a/elastalert/ruletypes.py
+++ b/elastalert/ruletypes.py
@@ -448,15 +448,16 @@ def get_spike_values(self, qk):
         extending ref/cur value retrieval logic for spike aggregations
         """
         spike_check_type = self.rules.get('metric_agg_type')
-        if spike_check_type in [None, 'sum', 'value_count', 'cardinality', 'percentile']:
-            # default count logic is appropriate in all these cases
-            return self.ref_windows[qk].count(), self.cur_windows[qk].count()
-        elif spike_check_type == 'avg':
+        if spike_check_type == 'avg':
             return self.ref_windows[qk].mean(), self.cur_windows[qk].mean()
         elif spike_check_type == 'min':
             return self.ref_windows[qk].min(), self.cur_windows[qk].min()
         elif spike_check_type == 'max':
             return self.ref_windows[qk].max(), self.cur_windows[qk].max()
+ 
+        # default count logic is appropriate in all other cases
+        return self.ref_windows[qk].count(), self.cur_windows[qk].count()
+
 
     def clear_windows(self, qk, event):
         # Reset the state and prevent alerts until windows filled again
diff --git a/tests/rules_test.py b/tests/rules_test.py
index 137bab13..7d83a224 100644
--- a/tests/rules_test.py
+++ b/tests/rules_test.py
@@ -18,6 +18,7 @@
 from elastalert.ruletypes import NewTermsRule
 from elastalert.ruletypes import PercentageMatchRule
 from elastalert.ruletypes import RuleType
+from elastalert.ruletypes import SpikeMetricAggregationRule
 from elastalert.ruletypes import SpikeRule
 from elastalert.ruletypes import WhitelistRule
 from elastalert.util import dt_to_ts
@@ -1400,3 +1401,29 @@ def test_comparerule_compare():
         assert False
     except NotImplementedError:
         assert True
+
+
+def test_spike_percentiles():
+    rules = {'buffer_time': datetime.timedelta(minutes=5),
+             'timeframe': datetime.timedelta(minutes=5),
+             'timestamp_field': '@timestamp',
+             'metric_agg_type': 'percentiles',
+             'metric_agg_key': 'bytes',
+             'percentile_range': 95,
+             'spike_type': 'up',
+             'spike_height': 1.5,
+             'min_threshold': 0.0}
+
+    rule = SpikeMetricAggregationRule(rules)
+
+    payload1 = {"metric_bytes_percentiles": {"values": {"95.0": 0.0}}}
+    timestamp1 = datetime.datetime.now() - datetime.timedelta(minutes=600)
+    data1 = {timestamp1: payload1}
+    rule.add_aggregation_data(data1)
+    assert len(rule.matches) == 0
+
+    payload2 = {"metric_bytes_percentiles": {"values": {"95.0": 9879.0}}}
+    timestamp2 = datetime.datetime.now()
+    data2 = {timestamp2: payload2}
+    rule.add_aggregation_data(data2)
+    assert len(rule.matches) == 1